How Advanced Image Processing Helps For SAR Image Restoration and Analysis

By Florence Tupin

I. OVERVIEW

The past few years have seen important advances in remote sensing imagery. The new sensors have improved resolutions in all dimensions, spatial resolution with reduced pixel sizes, temporal resolution with shorter revisit times and spectral resolution with increased number of spectral bands. With these new specifications, new challenges have appeared. The huge amount of remote sensing data raises new computational issues [1] and asks for faster processing approaches. New applications are accessible or can achieve new results like change detection, natural disaster monitoring, urban and landscape planning, biomass measurement. Theses advances are especially true for SyntheticAperture Radar (SAR) sensors, withmetric resolution available for civil satellite data, new spectral bands (L bandwith ALOS, X band for TerraSAR-X and COSMO-SkyMed), new interferometric potential thanks to TanDEM-X [2], reduced revisit time with constellations like COSMO-SkyMed. In spite of these improvements, SAR images remain difficult to interpret. New difficulties arose with the increase of spatial resolution: previously unnoticeable targets are now visible, bright scatterers are more numerous. Beyond speckle noise intrinsic to coherent imagery, geometric distortions due to distance sampling limit our visual understanding of such images, and direct interpretation of an urban area imaged by a SAR sensor is still reserved to expert photo-interpreters.

Together with progress made with recent sensors, new powerful image processing methods have emerged in the recent years. Among the major advances made last decade by the image processing and computer vision communities, we have chosen to emphasize three of them for their long-term potential and applicative interest for SAR imaging.

The first family of advances in signal and image processing is related to the progress in statistical modeling of multiplicative noise, which is particularly important to deal with SAR imagery. Therefore, the first point we would like to mention is the Mellin framework proposed in [3] to deal with positive random variables and their multiplication.

The second family of methods is based on the idea of “patches”. Patches are small image parts (typically 5 × 5 or 7 × 7 pixels). They capture fine scale information such as texture, bright dots or edges. Given their very local extent, they are highly redundant, i.e., many similar patches can be found in an image. These similar patches can then be combined to reduce noise [4]. But patch similarity can also be applied to stereovision or change detection.

The third family are the “graph-cut” approaches, where an image processing problem is converted into the search of a minimumcut in a graph [5]. Efficientminimumcut algorithms have been proposed for computer vision problems [6] and the focus is put on designing a graph to solve a given image processing task. Theses approaches have been mainly used to optimize functionals or energies derived from Markovian modeling or regularization approaches. A famous model is the Total Variation minimization [7] which can be exactly minimized in one of its discrete form using a multiple layers graph [8], [9]. Graphcut based approaches have also become very popular for many denoising and partitioning problems.

We will see in this letter how these three theories (among others) have contributed to the development of efficient tools for SAR image processing.

II. SAR DATA STATISTICAL MODELING

One of the main difficulties of SAR imagery is the speckle phenomenon. Radar are coherent imagery systems, leading to interferences between electro-magnetic waves backscattered by the reflectors inside a pixel. These interferences cause a strong variability of radiometric values, even for a physically homogeneous area. In his seminal work [10], Goodman has derived the gray level distributions of radar images: Rayleigh distribution of amplitude image, Nakagami for multi-looked data (multilook meaning that some pixels have been averaged), Gamma for multi-looked intensity image. However, these models have shown some limits when dealing with high resolution images. Since the beginning of SAR images, many distributions have been proposed to model radar data: K distribution [11], lognormal distribution, Weibull distribution etc. These distributions can be well adapted to some specific cases. They are usually defined by some parameters that have to be empirically learnt on some small local areas of the images. The tradeoff between bias and variance of the estimators requires large window sizes while keeping a homogeneous statistical population.

In the past recent years, a powerful framework has been developed by J.-M. Nicolas to unify the set of distributions and to provide efficient tools to compute parameter estimators [3]. The whole theory is built on the observation that radar amplitude or intensity is intrinsically positive. Therefore, the Fourier transform, which is an integral over the set of all real values, should be replaced by some transform defined on positive values only. This is the case of the Mellin transform, which has the following form:

where s is a complex number, and p stands here for the random variable distribution. Mimicking the characteristic function and all the definitions that can be derived from it, like moments and cumulants, a second kind characteristic function based on Mellin transform has been defined, leading to log-moments and log-cumulants. The Mellin convolution, which is the counterpart of the convolution in the positive value domain, provides a natural way to define the distribution of products of independent random variables (whereas the regular convolution deals with sum of variables). Without going too far into the details of this still evolving theory, we would like to mention what seems to us important contributions of this work. First, parameter estimation based on log-cumulants gives low variance estimators, allowing the use of analysis windows of reduced sizes (figure 1). Secondly, this work has enlightened the relationships between the different distributions (Gamma, K, inverse Gamma, Weibull, log-normal,…) thanks to Mellin convolution and thanks to a diagram defined by the second and third logcumulants (figure 2). Thirdly, the Fisher distribution has appeared as a “generic” distribution with 3 parameters adapted to a wide range of surfaces (urban areas, vegetation, etc.) [12].

This work has been first developed for amplitude or intensity images, and has been adapted later by different authors to polarimetric data. We would like to mention the work of Anfinsen on the extension of the use ofMellin transform for polarimetric data by developing the matrix-variate Mellin transform framework, and exploiting it to better process polarimetric data [13].

III. SAR DATA DENOISING

Whereas the Mellin framework takes into account the variability of the scene within a region with a variety of distributions seen as Mellin products, denoising approaches try to suppress signal-dependent speckle variability to recover the scene reflectivity.

Non-local approaches and graph-cut based optimization have proven to lead to very efficient denoising methods. We will illustrate in this section how these recent and popular image processing approaches can be adapted to the case of SAR images.

A. Non-local approaches
The first family of methods described in the introduction is based on patch similarity. They are known as non-local approachesn or NL-means [4]. The main idea of non-local methods is to find similar patches in the image. In the case of image denoising, this set of similar patches is then used to suppress the noise, for instance by averaging the central pixels of each patch.

Let us consider the Gaussian filter for comparison. Its principle is to average spatially close pixels to suppress the noise. Spatially close pixels can belong to different populations, though. Therefore, improvements of this basic idea have been proposed. Instead of taking “spatially close” pixels, we can take “radiometrically close” pixels [4]. In this case, the problem is to select a pixel which should be “radiometrically” close from another pixel. And here comes the idea of patch comparison. A pixel can reasonably be assumed to be radiometrically close from another one, if their surrounding patches are similar (see figure 3). To denoise a pixel s, the values of pixels t are averaged with a weight depending on the similarity of the two patches surrounding s and t. This is a powerful approach since there is no connectivity constraint between s and t compared to [14], [15], and far apart patches can be considered to denoise a given pixel (hence the term “non-local” denoising).

This framework has been initially developed for Gaussian noise: the denoising is done by averaging the noisy samples, and the similarity criterion is based on the Euclidean distance between the two patches. To adapt this framework to other kinds of noise while keeping the principle of patch comparison, Deledalle et al. have proposed a probabilistic framework [16]. The denoising task is expressed as a weighted maximum likelihood estimation, and the weight definition is established thanks to a probabilistic approach. Besides, this probabilistic framework leads to similarity weights formed by two terms, one related to the noisy data (likelihood similarity) and the other one
to the denoised data (prior similarity). For this second term, an iterative scheme has been proposed which greatly improves the results when strong noise is present on the data. This framework can be applied to any noise having a known distribution like Gamma or Poisson. In the case of SAR amplitude images, the denoising scheme is the following:

The final algorithm is thus rather simple and results are interesting, with preserved edges and smoothed areas as can be observed on figure 4.

Other efficient denoising methods have been proposed in the recent years like wavelet based methods [18], [19], [20] or BM3D based approaches [21]. One of the strengths of the proposed probabilistic framework is that it allows the application of non-localmethods for complex data or vectorial data as soon as noise is well modeled by a parametric distribution. Thus, it can be used efficiently to process interferometric or polarimetric data using the speckle noise described by a zero-mean complex circular Gaussian distribution [10]. For instance in the case of interferometric images, weighted likelihood estimators for reflectivity, interferometric phase and coherence are derived, and the weights measure the probability that the observations come from the same parameters for all the couples of pixels of the two patches. Figure 5 illustrates the potential of such approaches. Instead of computing local hermitian products to derive interferometric information and thus losing spatial resolution, such approaches can be used to compute interferograms at the nominal resolution of the data. The case of polarimetric data is similar with the estimation of the underlying covariance matrix. Application of such a framework is described in [22].

Beyond the denoising application, patch similarity of amplitude, interferometric or polarimetric data can be very useful for change detection or movement monitoring.

Fig. 4. Illustration of the NL-means SAR denoising. Figure a) on the left is a 100-looks image obtained by multi-looking a Very High resolution image (image acquired by ONERA, multi-looked by CNES ONERA CNES). This image can be considered as a ground truth. Figure b) is a 1-look image of resolution 1×1 meter. Figure c) is the denoised version of the 1-look image b). Fine details are well preserved by this approach.

Fig. 5. Illustration of NL-InSAR. On the top, the original interferometric data (amplitude, phase and coherence, with 1-look). On the bottom, the non-local estimation of amplitude, phase, and coherence with no loss of resolution.The weights of the likelihood estimations are computed using the similarity of the complex patches of the two interferometric images. Results are from [17].

B. Regularization Approaches
Other powerful approaches for denoising are regularization based methods which have also been extensively studied in the past 10 years in the image processing and computer vision communities. The idea is to express the problem as an energy minimization one, the energy being divided into two terms, one related to the noise distribution (likelihood term) and the other one to the properties we expect for the solution (prior term). This energy can be derived for instance by a probabilistic approach (discrete point of view), but also from variational methods establishing a functional to minimize (continuous point of view). The likelihood term is usually linked to the model of noise perturbating the data. The prior term or regularization term usually imposes the “smoothness” of the solution and is expressed through interactions between neighboring pixels. A popular model is a low total variation (TV model [7]) corresponding to almost piecewise constant image or equivalently to a sparse gradient (only few values of the gradient can be non zero). But other models like truncated quadratic or phifunctions can be chosen [23].

Beyond the difficult choice of the right model to express our prior knowledge on the scene, the minimization of the energy or functional is generally not easy. Indeed, for many cases, and especially for radar imagery, the neg-log-likelihood is not convex. In this case, usual continuous optimization methods similar to gradient descent can not be applied or risk to get stuck in a local minimum. Recent approaches of combinatorial optimization based on graph-cut allow for exact optimization of energies composed of a convex prior term (like TV minimization) and a (possibly non-convex) data term [8], [9]. Theses approaches build a multiple layer graph, each layer corresponding to a possible gray level of the solution and search for the minimum cut in this graph. The minimum cut gives the exact solution of the optimization problem in the discrete space (spatially discrete image and discrete gray level set). There are two main limitations to this important result. The first one is the quantization of the gray levels which may not be easy for high dynamic images like SAR data. It can be solved by combining a discrete optimization step and a continuous one [24]. The second limit is the memory size. Indeed, the size of the graph is the size of the image multiplied by the number of considered gray levels and it should be stored in memory for the minimum cut computation. This size is prohibitive for remote sensing images and block cutting is not an acceptable solution. Recent approaches based on multi-label partition moves [25] or dichotomy [26] largely reduce the memory cost, but loosing the optimality guarantee.

These models can bring interesting results for SAR imagery.The first application is the amplitude denoising of a radar image. In this case, adapted prior can be defined. In [27], the scene is decomposed as the sum of two terms, a component with low total variation representing the “background” of the scene in a cartoon-like model, and a sparse component representing the bright scatterers of the image with few non zero pixels. This model can be solved exactly using graph-cut optimization. Another interesting application is the joint regularization of phase and amplitude of InSAR data [28]. In this case, it is possible to take into account the exact distribution of the Mlook interferometric data for the likelihood term, and to introduce some prior knowledge preserving simultaneously phase and amplitude discontinuities. The phase and amplitude information are hopefully linked since they reflect the same scene. Amplitude discontinuities thus usually have the same location as phase discontinuities and conversely. To combine the discontinuities, a disjunctive max operator has been used, providing well preserved fine structures [28]. Figure 6 shows an example of 3D reconstruction using a joint regularization of the interferometric phase.

These approaches can also be particularly useful for multichannel phase unwrapping [29]. Indeed, they provide a very efficient way to combine different interferometric phases in a multi-modal likelihood term, whereas a regularization term imposes to the unwrapped phase some smoothness constraints. It is also possible to introduce atmospheric corrections in the optimization scheme in an iterative way. These approaches could provide a highly flexible framework to introduce prior knowledge in Digital Terrain Model reconstruction in multi-channel interferometry or in ground movement monitoring in differential interferometry [30]. Figure 7 illustrates the global combination of multi-baseline interferograms with automatic atmospheric corrections using an affine model of phase variation with elevation [31].

IV. DISCUSSION AND CONCLUSION

We have tried to illustrate in the previous sections how advanced image processing methods which have been recently developed by the computer vision community can help SAR image processing. We have focused on three of them, distribution modeling, non-local methods, regularization approaches with graph-cut optimization. Of course, the cited references are far from being exhaustive on these different subjects and other methods like wavelets-based methods would have deserved a more detailed presentation.

Another recent and powerful theory which might well have a great impact in the coming years is compressive sensing [32], [33]. This theory has shown that, despite Shannon theory, for many signals only few measurements are required to allow a faithful reconstruction, provided the signal has a sparse representation in a suitable space (i.e., few non-zero coefficients in that representation). Reconstruction of sparse signals has a long history in radar literature. Recent results in compressed sensing have fueled several works in the areas of compressed SAR acquisitions systems [34], SAR tomography [35] and for SAR GMTI data [36] to cite only a few. We refer the reader to the recent review [37] for more on this very active subject.

Nevertheless, whatever the progress for low-level tasks such as denoising, it is unlikely that they will allow SAR image understanding without high level methods. The influence of geometric configurations combined with distance sampling is predominant on the appearance of the objects in the image. Therefore, a step of object recognition highlighting the relationship between the different signals is usually necessary to fully understand SAR information. Many works have been led in this direction like [38] for optical data, or [39], [40], [41] exploiting jointly SAR and optical images, or an external database. The object level that could be available with metric resolution is still difficult to reach with SAR images on their own. Dictionaries and learning methods could provide some keys for the next step of understanding.

V. ACKNOWLEDGMENTS

I would like to thank Jean-Marie Nicolas for our long collaboration, Lo¨ıc Denis and J´erˆome Darbon for our more recent ones. Special thanks for all the past or actual members of the SAR team of Telecom ParisTech, but particularly to the PhD students Charles Deledalle, Aymen Shabou and Helene Sportouche, whose results have illustrated this letter. Acknowledgments also to ONERA and CNES for providing the images.

REFERENCES
[1] A. Plaza. Computational issues in remote sensing data analysis. IEEE
Geoscience and Remote Sensing Newsletter, (156):11–15, 2010.
[2] M. Zink. TanDEM-X: close formation achieved. IEEE Geoscience and
Remote Sensing Newsletter, (157):23–25, 2010.
[3] J.M. Nicolas. Introduction aux statistiques de deuxi`eme esp`ece : applications
des log-moments et des log-cumulants `a l’analyse des lois
d’image radar. Traitement du signal (french peer review journal;
translated by S. Anfinsen ”Introduction to Second Kind Statistics: Application
of Log-moments and log-cumulants to Analysis of Radar
Images” (http://eo.uit.no/publications/JMN-TRANS-10.pdf), 19(3):139–
167, 2002.
[4] A. Buades, B. Coll, and J.M. Morel. Nonlocal Image and Movie Denoising.
International Journal of Computer Vision, 76(2):123–139, 2008.
[5] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy minimization
via graph cuts. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 26(2):147–159, 2001.
[6] Y. Boykov and V. Kolmogorov. An experimental comparison of mincut/
max-flow algorithms for energy minimization in vision. Pattern Analysis
and Machine Intelligence, IEEE Transactions on, 26(9):1124–1137,
2004.
[7] L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise
removal algorithms. Physica D, 60:259–268, 1992.
[8] H. Ishikawa. Exact optimization for Markov random fields with convex
priors. IEEE Trans. on Pattern Analysis and Machine Intelligence,
25(10):1333–1336, oct 2003.
[9] J. Darbon and M. Sigelle. Image restoration with discrete constrained Total
Variation part I: Fast and exact optimization. Journal of Mathematical
Imaging and Vision, 26(3):261–276, December 2006.

[19] S. Solbo and T. Eltoft. Homomorphic wavelet-based statistical despeckling
of SAR images. IEEE Transactions on Geoscience and Remote Sensing,
42(4):711–721, 2004.
[20] T. Bianchi, F. Argenti, and L. Alparone. Segmentation-Based MAP Despeckling
of SAR Images in the Undecimated Wavelet Domain. IEEE
Transactions on Geoscience and Remote Sensing, 46(9):2728–2742,
2008.
[21] S. Parrilli, M. Poderico, C.V. Angelino, G. Scarpa, and L. Verdoliva. A
non local approach for SAR image denoising. IEEE International Conference
on Geoscience and remote Sensing (IGARSS), 2010.
[22] C.-A. Deledalle, F. Tupin, and L. Denis. Polarimetric SAR estimation
based on non-local means. IEEE International Geoscience and Remote
Sensing Symposium (IGARSS), 2010, pages 2515–2518, 2010.
[23] A. Blake and A. Zisserman. Visual Reconstruction. MIT Press, 1987.
[24] A. Shabou, J. Darbon, and F. Tupin. A Markovian Approach for InSAR
Phase Reconstruction WithMixed Discrete and Continuous Optimization.
IEEE Geoscience and Remote Sensing Letters, pages 526–530, 2010.
[25] A. Shabou, J. Darbon, and F. Tupin. A graph-cut based algorithm for approximate
MRF optimization. IEEE International Conference on Image
Processing (ICIP), 2009, pages 2413–2416, 2009.
[26] L. Denis, F. Tupin, J. Darbon, and M. Sigelle. SAR Image Regularization
with Fast Approximate Discrete Minimization. IEEE Transactions on
Image Processing, 18(7):1588–1600, 2009.
[27] L. Denis, F. Tupin, and X. Rondeau. Exact discrete minimization for
TV+L0 image decomposition models. IEEE International Conference on
Image Processing (ICIP), 2010.
[28] L. Denis, F. Tupin, J. Darbon, and M. Sigelle. Joint Regularization of
Phase and Amplitude of InSAR Data: Application to 3D reconstruction.
IEEE Transactions on Geoscience and Remote Sensing, 47(11):3774 –
3785, 2009.
[29] V. Pascazio and G. Schirinzi. Multifrequency InSAR height reconstruction
through maximum likelihood estimation of local planes parameters.
IEEE Transactions on Image Processing, 11(12):1478–1489, 2002.
[30] A. Shabou. Multi-label MRF Energy Minimization with Graph-cuts: application
to Interferometric SAR Phase Reconstruction. PhD thesis, Telecom
ParisTech, France, 2010.
[31] F. Chaabane, A. Avallone, F. Tupin, P. Briole, and H. Maˆıtre. Multitemporal
correction of tropospheric effects in differential sar interferometry.
IEEE Transactions on Geoscience and Remote Sensing, may 2006.
[32] E. Cand`es. Compressive sampling. Int. Congress of Mathematics,
3:1433–1452, 2006.
[33] D. Donoho. Compressed sensing. IEEE Trans. On Information Theory,
52(4):1289–1306, 2006.
[34] G. Rilling, M. Davies, and B. Mulgrew. Compressed sensing based compression
of SAR raw data. SPARS’09 Signal Processing with Adaptive
Sparse Structured Representations, 2009.

April 25, 2011 – 2:45 pm | By dev | Posted in News, Newsletter | Comments (0)

New software tools for NASA Earth Science

Tools to Support the Reuse of Software Assets for the NASA Earth Science Decadal Survey Missions

Chris A. Mattmann1,4, Robert R. Downs2, James J. Marshall3,
Neal F. Most3, Shahin Samadi3

1Jet Propulsion Laboratory
California Institute of Technology
Pasadena, CA 91109, USA		 2CIESIN

Columbia University
Palisades, NY 10964 USA
3INNOVIM, NASA GSFC

Mail Stop 614.9
Greenbelt, Maryland 20771 USA

 4Computer Science Department
University of Southern California
Los Angeles, CA 90089, USA

chris.a.mattmann@nasa.gov http://www.esdswg.com/softwarereuse

Introduction

The future Earth science missions at the National Aeronautics and Space Administration (NASA) promise to provide an explosion of data and a platform for science that previously was unachievable using existing hardware, software, and assets. Instrument resolution is increasing, as is the ability of software and hardware to deal with data volumes that will easily grow to the 10–100 petabyte range in the next five years [1]. Over the past twenty years, NASA has invested in software to support all phases of the Earth science mission pipeline. These investments include components and architectures that support science data processing at Science Investigator-led Processing Systems (SIPS), data archival and dissemination at the Distributed Active Archive Centers (DAACs), and ad-hoc data analyses and custom product generation using DAAC-provided data [2]. This general flow is shown in Fig. 1.

Fig 1. The NASA Earth Science Context. Data is taken by and sent to ground stations, which move the data to SIPS. DAACs are responsible for long-term archiving of the information, and dissemination. Ad-hoc analyses occur in the ACCESS and MEaSUREs programs.

For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) Data Processing System (MODAPS) has evolved over time to support higher data processing rates and the production of data products for additional Earth-observing instruments by enhancing its architecture [3]. In addition, several recent efforts [4] to standardize process management and control for both the Orbiting Carbon Observatory (OCO) missions, as well as the NPOESS Preparatory Project (NPP) joint NASA–NOAA–DOD missions, have also demonstrated the utility in the reuse of software assets.

However, to date the aforementioned efforts are the exception and not the norm. Many Earth science data system components and architectural patterns are reconstructed for each mission. There have been a number of reasons for this practice including: (1) the distributed scientific expertise of NASA, (2) the desire to have that expertise co-located with the data as it is processed and delivered for wide dissemination, (3) procurement practices, where contract and equipment resources are stove-piped into separate contracts and programs, and (4) each scientific community purports a unique set of requirements for data processing and data products, that may not easily lend itself to justify reuse.

The paradigm of NASA missions is changing, primarily due to the upcoming missions identified in the National Research Council’s Earth Science and Applications from Space decadal survey [5] (as well as other future “decadal-like” missions). It is even more imperative that NASA look to reduce costs, increase software productivity, explore areas for consolidation of homogeneous services, and ultimately promote and facilitate a culture of reusing successful software assets and patterns across its missions.

Software reuse can help inform the successful design of future NASA missions in a number of different ways, in particular through: (1) identification and selection of existing, proven Earth science software components (or software components applicable in Earth science data systems) whose reuse saves development costs and time, (2) application of existing architectural styles and patterns [6] that induce specific quality attributes (reliability, scalability, etc.) in the resultant software, and (3) identification of new assets developed for missions which are of broader applicability, and themselves should be disseminated to the community.

Reusable software artifacts are not limited to just code. These assets may include algorithms and models, architectures and design patterns, systems modules and scripts, technical documentation and test results, and use metrics as well as other artifacts produced during the software development life cycle.

The NASA Earth Science Data Systems (ESDS) Software Reuse Working Group (SRWG) is chartered with the investigation, production, and dissemination of information related to the reuse of NASA Earth science software assets. One major current objective is to engage the NASA decadal missions in areas relevant to software reuse.

Table 1. Summary of Reuse Readiness Levels (RRLs)

Level Summary
RRL 1 Limited reusability; the software is not recommended for reuse.
RRL 2 Initial reusability; software reuse is not practical.
RRL 3 Basic reusability; the software might be reusable by skilled users at substantial effort, cost, and risk.
RRL 4 Reuse is possible; the software might be reused by most users with some effort, cost, and risk.
RRL 5 Reuse is practical; the software could be reused by most users with reasonable cost and risk.
RRL 6 Software is reusable; the software can be reused by most users although there may be some cost and risk.
RRL 7 Software is highly reusable; the software can be reused by most users with minimum cost and risk.
RRL 8 Demonstrated local reusability; the software has been reused by multiple users.
RRL 9 Demonstrated extensive reusability; the software is being reused by many classes of users over a wide range of systems.

In this paper we report on the current status of these activities. First, we provide some background on the SRWG in general and then discuss the group’s flagship recommendation, the NASA Reuse Readiness Levels (RRLs). We continue by describing areas in which mission software may be reused in the context of NASA decadal missions. We conclude the paper with pointers to future directions.

Working Group Background

The NASA Earth Science Data Systems (ESDS) Software Reuse Working Group is chartered with the promotion and identification of software assets targeted for reuse in NASA’s Earth Science Data System pipeline [7]. The group is focused on architectures and technologies that facilitate software reuse. In particular, we are investigating software components and architectures developed to enable cloud and grid computing capabilities, as well as cyberinfrastructure for using mission and scientific data.

The flagship product of the group to date is a focused set of NASA Reuse Readiness Levels (RRLs), which have been released and are now available for use [8]. The RRLs, similar to the NASA Technology Readiness Levels (TRLs) for technology, are a nine-level guide that can be used to rank and compute the reusability of a software asset [9]. A summary of the RRLs, taken from [8], is shown in Table 1.

Besides the RRLs, the Software Reuse Working Group (SRWG) is also working on the development of case study documents describing efforts to leverage the RRLs in the assessment of two areas of NASA mission software: (1) the methodology and suitability of existing NASA software assets for inclusion in a mission, and (2) the identification, curation, and dissemination of software assets that are being developed as part of a NASA mission that can be included in future missions. In addition, the SRWG is working on a recommendation for the packaging of reusable software assets to facilitate distribution, covering an information model for software packaging, and a classification/comparison of the state of the art in software packaging techniques.

Both of the aforementioned documents are considered works-in-progress, and both of the documents include input from current NASA decadal missions, including the Soil Moisture Active & Passive (SMAP) mission and the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) mission. We also are actively working with other Tier-1 NASA decadal missions including the Orbiting Carbon Observatory-2 mission, and the Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission to best determine how and where reusable software assets could be leveraged. We plan to support the upcoming Tier-2 missions as they begin to ramp up as well. In the next section we will provide greater detail about the NASA Reuse Readiness Levels (RRLs) and their applicability to NASA decadal missions.

Fig 2. The Reuse Readiness Level Web Calculator. Users input an associated weight and score for each of the RRL topic area levels and an RRL is computed and displayed as a weighted average of those calculations. The associated RRL description is shown in yellow at the bottom of the calculator.

Reuse Readiness Levels

The NASA Reuse Readiness Levels (RRLs) have been developed for use as a measure to evaluate the potential reusability of software. The RRLs can be used to assess software that is being developed or to assess software assets that are being considered for adoption. Software can be evaluated either by using the RRLs in a simple manner to obtain a rough assessment of the software or by using the RRLs more extensively to obtain a precise assessment, which would include an assessment of the software in terms of nine topic areas.

Using the RRLs in a simple manner, the software under evaluation is compared to the brief summary descriptions of the RRLs to determine a value, from 1 to 9, that reflects the level of the potential reusability of the software. The RRLs can be used in this way to attain a quick assessment, which lacks precision, but may be appropriate for attaining efficient assessments when comparing many competing software candidates or when only a rough estimate of the potential reusability of a software product is required.

Alternatively, extensive use of the RRLs can be applied by using a 9×9 grid to evaluate the software against each of the topic areas to determine the level of maturity that the software has attained for each of the nine topic areas. Using the RRLs in this extensive manner can be more time consuming, but enables assessment of each topic area to identify areas where additional development may be required to meet the needs of a particular software project. Prior to using the RRLs in this manner, the software requirements of the project should be identified for each topic area so that the level of effort necessary to improve the software to an acceptable level for each topic area can be determined.

A calculator is being developed for use with the RRLs (a web-based prototype of this calculator is shown in Fig. 2). Using the RRL calculator, weights can be established for each topic area, depending on the importance of a particular topic area to meet the requirements of a particular software project, and an average overall RRL value can be calculated from assessments of topic area levels. A more advanced version of the RRL calculator, which may offer more features and/or guidance on assessing software assets, is under consideration. The SRWG has also received a copy of a Microsoft Excel-based calculator tool, developed by modifying an existing TRL calculator [10], from a member of the software reuse community. We are examining this tool to ensure that it correctly captures the information contained in the current release of the RRL document.

Fig 3. Using the SRWG RRLs and RES to design and implement NASA decadal missions.

Tools such as the RRL calculator enable a structured evaluation of reusable assets as software producers and consumers measure applicability and compatibility for their particular project. We are exploring the integration of the RRL calculator with our Reuse Enablement System (RES) [11], a software portal used to track and disseminate information about reusable software assets. The RES system is currently being deployed by the Soil Moisture Active & Passive (SMAP) mission as a proof of concept as shown in Fig. 3. In the following section, we will describe the relationship of RRLs and associated software reuse tools to that of the NASA decadal missions.

Reuse of Mission Software

The reuse of software offers opportunities for the new decadal survey missions and future space missions to reduce costs and improve the quality of the software that is either produced by or used from previous efforts. Likewise, software reuse offers opportunities to obtain similar benefits when processing and reprocessing data obtained from such missions. Recipients of the NASA ESDS Software Reuse Working Group Peer-Recognition Software Reuse Award [12] have demonstrated the contribution of new reusable assets and the utilization of existing reusable assets in systems development for NASA missions; for example, the National Polar-orbiting Operational Environmental Satellite System Preparatory Project’s Science Data Segment reused a variety of system components to reduce development effort and help ensure reliability [13], as did the Orbiting Carbon Observatory’s (OCO) Ground Data System [4].

In conjunction with the ICESat-2 mission effort, procedures and templates also are being developed to use the RRLs to assess the current state of readiness when assessing software from previous missions for potential reuse in future missions. Using such tools can help to improve the usability of software created during previous missions. Such tools also can be used to assess the potential reusability of software that is being developed for new missions to improve its potential for reusability in other future missions. The SRWG plans to work with the ICESat-2 team as needed to help them assess some of the existing software assets from the original ICESat mission that they plan to reuse.

In addition, using tools, such as calculators (as shown in Fig. 2), templates, and procedures, in conjunction with the RRLs, to assess the reusability of software, can identify aspects of the RRLs that may be considered for possible improvement. ICESat-2’s experience will enable a use case study to help the SRWG improve the RRLs and how they are used to perform software reusability assessments. Likewise, testing the use of such tools for assessing the potential reuse of software also will contribute to their refinement and inspire the development of additional tools for assessing reusability [14, 15, 16] and can foster the consumer’s confidence that the asset has been assessed as to its level of robustness and readiness for operational use.

Conclusions

Considering the data processing needs of the new decadal survey missions, the reuse of software from previous missions offers an opportunity to leverage the investments made in previous missions. The RRLs have been developed by the ESDS SRWG to assess the readiness of software for potential reuse. Using the RRLs in conjunction with other tools, such as the RRL calculator, templates, procedures, and lessons learned, can improve capabilities for reusing software in new missions and for realizing the benefits of software reuse.

In addition to reusing software and system artifacts from previous missions in the new missions, software reuse offers an opportunity for the decadal survey missions to develop software that can be used in other future missions. Planning for the potential reuse of new software can complement the efforts of reusing previously developed software. Adopting a systematic approach to software reuse can contribute to the improvement of software development practices and to the potential reuse of software and other system artifacts in the future [15].

The use of tools to assess the reusability of software and to register and describe software for potential reuse offers benefits for organizations that develop software for potential reuse and for those that reuse existing software. The use of such tools for the decadal survey missions can assist in the preparation of software that was developed for use in previous missions for possible reuse in future missions. In addition, these tools also can help to prepare software that is being developed for the new missions for use in future missions.

Software assets considered as candidates for potential reuse can be registered and described in a RES where they can be found and analyzed by developers for inclusion in future systems. Refining such tools and developing additional tools to support the reuse of software can contribute to the capabilities available for both software producers and software adopters.

It is important for current missions to recognize that the systems and components they are currently developing may have the potential to be reused by future missions. Therefore, any steps they can take to make such assets more reusable will help encourage a more systematic reuse process, one that can continue to improve future missions through the realization of the benefits of software reuse.

Acknowledgement

The authors are grateful to the members of the National Aeronautics and Space Administration (NASA) Earth Science Data Systems Software Reuse Working Group who have contributed to the efforts described in this work. The authors also would like to thank Lorenzo Bruzzone and Chris Ruf for their helpful comments. Support was provided for Robert Downs under NASA contract NNG08HZ11C. This effort was supported in part by the Jet Propulsion Laboratory, managed by the California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

This article was adapted from the paper, “Reuse of Software Assets for the NASA Earth Science Decadal Survey Missions,” which was prepared by the authors for presentation at the IGARSS 2010 Conference and has been published in its proceedings.

References

  1. R. Yang and M. Kafatos, “Massive data sets issues in earth observing,” in Handbook of Massive Data Sets, J. Abello, P. M. Pardalos, and M. G. Resende, Eds. Norwell, MA: Kluwer Academic Publishers, 2002, pp. 1093­–1140, 2002.
  2. H. K. Ramapriyan, J. Behnke, E. Sofinowski, D. Lowe, and M. A. Esfandiari, “Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS),” in Standard-Based Data and Information Systems for Earth Observation, Berlin: Springer-Verlag, 2010, pp. 63–92.
  3. E. Masuoka, C. Tilmes, N. Devine, G. Ye, and M. Tilmes, “Evolution of the MODIS science data processing system,” in Geoscience and Remote Sensing Symposium, 2001. IGARSS ’01. IEEE 2001 International, vol. 3, pp. 1454–1457, 9–13 July 2001.
  4. C. Mattmann, D. Freeborn, D. Crichton, B. Foster, A. Hart, D. Woollard, S. Hardman, P. Ramirez, S. Kelly, A. Y. Chang, C. E. Miller, “A Reusable Process Control System Framework for the Orbiting Carbon Observatory and NPP Sounder PEATE missions,” in Proceedings of the 3rd IEEE Int’l Conference on Space Mission Challenges for Information Technology (SMC-IT 2009), pp. 165–172, July 19–23, 2009.
  5. National Research Council, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. Washington: National Academies Press, 2007.
  6. R. N. Taylor, N. Medvidovic, and E. M. Dashofy, Software Architecture: Foundations, Theory, and Practice. USA: John Wiley & Sons, 2009.
  7. J. J. Marshall, R. R. Downs, S. Samadi, N. S. Gerard, and R. E. Wolfe, “Software reuse to support earth science,” Journal of Frontiers of Computer Science and Technology, vol. 2, no. 3, pp. 296–310, May 2008.
  8. NASA Earth Science Data Systems Software Reuse Working Group. (2010, April 30). Reuse Readiness Levels (RRLs), Version 1.0 [Online] Available: http://www.esdswg.org/softwarereuse/Resources/rrls/
  9. J. J. Marshall and R. R. Downs, “Reuse Readiness Levels as a Measure of Software Reusability,” Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International, vol. 3, pp. III-1414–III-1417, 7–11 July 2008.
  10. W. Nolte. (2010, July 23). TRL Calculator Upgrade to v 2.2 [Online] Available: https://acc.dau.mil/CommunityBrowser.aspx?id=25811
  11. J. J. Marshall, R. Downs, C. Mattmann, “Progress Towards a NASA Earth Science Reuse Enablement System (RES),” in Information Reuse and Integration (IRI), 2010 IEEE International Conference on, pp. 340–343, 4–6 Aug. 2010.
  12. NASA Earth Science Data Systems Software Reuse Working Group, “Peer-Recognition Software Reuse Award Recipients,” Available: http://www.esdswg.com/softwarereuse/Resources/awards/reuse-award-recipients/, October 27, 2010.
  13. S. Samadi, R. Gerard, M. Hunter, J. J. Marshall, R. J. Schweiss, R. E. Wolfe, E. J. Masuoka, “Reusing Software to Build Data Processing Systems: NPP Science Data Segment Case Study,” in Aerospace Conference, 2007 IEEE, pp.1–12, 3–10 March 2007.
  14. J. J. Marshall, R. R. Downs, and S. Samadi, “Relevance of Software Reuse in Building Advanced Scientific Data Processing Systems,” in Earth Science Informatics, vol. 3, no. 1, pp. 95–100, June 2010.
  15. J. J. Marshall, R. R. Downs, and S. Samadi, “Building the Next Generation of Aerospace Data Processing Systems by Reusing Existing Software Components,” in Aerospace Technologies Advancements, T.T. Arif, Ed. Croatia: IN-TECH, 2010, pp. 25–36.
  16. R. R. Downs and J. J. Marshall. (2010, July). A Proposal on Using Reuse Readiness Levels to Measure Software Reusability. Data Science Journal [Online]. 9. pp. 73–92. Available: http://dx.doi.org/10.2481/dsj.009-007.
March 31, 2011 – 8:39 pm | By william | Posted in Newsletter, Research Highlights | Comments (0)

WHISPERS 2010

2nd Workshop on Hyperspectral Image and Signal Processing – Evolution in Remote Sensing

http://www.ieee-whispers.com

Summary report by Jón Atli Benediktsson, Jocelyn Chanussot and Björn Waske

The Second Workshop on Hyperspectral Image and Signal Processing – Evolution in Remote Sensing (WHISPERS) was held at the campus of the University of Iceland, Reykjavik, Iceland June 14-16, 2010. WHISPERS 2010 received the technical sponsorship of the IEEE Geoscience and Remote Sensing Society (GRSS) and financial sponsorships from the University of Iceland and the IEEE Iceland Section. Organized in two parallel tracks over three days, the workshop was a great success, gathering 160 researchers from 30 different countries worldwide.

A total of 161 papers were submitted to WHISPERS 2010 (both regular submissions and special session submissions), 140 of which were accepted, corresponding to a 13 % rejection rate. Ninety oral presentations organized in 18 sessions were given at the workshop, and 50 posters organized in 6 sessions were presented in interactive sessions. The evaluation of all the papers was performed based on the reports of anonymous reviewers. On average, each paper received 2.5 reviews. All the papers published at WHISPERS 2010 are available on IEEE Xplore.

The technical program also featured three outstanding plenary talks delivered by three prestigious and highly recognized experts:

  • Dr Alan Schaum, from the Naval Research Laboratory, USA, delivered a talk entitled „the Continuum Fusion, a new Theory of Inference.”

  • Dr Xiuping Jia, from the School of Engineering and Information Technology University College, The University of New South Wales, Australian Defence Force Academy, Australia, delivered the talk “Feature Mining from a Hyperspectral Data Cube for Information Mapping: 3D and Beyond.”

  • Dr David G. Goodenough, from the Department of Computer Science, University of Victoria, Canada, delivered the talk “Hyperspectral Applications for Forestry.”

Three papers were selected to receive a Best Paper Award. The authors received one copy of the greatly sought-after “golden whispers” trophy, and a certificate of recognition during a memorable banquet. Congratulations go to:

  • Gabriele Moser and Sebastiano B. Serpico for their outstanding contribution “A Markovian Generalization of Support Vector Machines for Contextual Supervised Classification of Hyperspectral Images”

  • Joel Kuusk and Andres Kuusk for their outstanding contribution “Autonomous Lightweight Airborne Spectrometers for Ground Reflectance Measurements”

  • Iryna Danilina, Alan R. Gillespie, Matthew Smith, Lee Balick and Elsa Abbott for their outstanding contribution “Thermal Infrared Radiosity and Heat Diffusion Model Verification and Validation”.

The aim of the WHISPERS workshop is to bring together all the people involved in spectroscopy and hyperspectral data processing, generally speaking.

By “data“, we mean : signals, as provided by spectrometers and processed individually, images, from the ground using microscopes and spectrometers to airborne or satellite sensors, up to astrophysical data and models: models of the sensors or of the sensed scene, including physical considerations.

By “processing“, we mean everything from the acquisition, the calibration to the analysis. People were invited to submit new research results on the following suggested topics: spectrometers and hyperspectral sensors (design and calibration), physical modeling, physical analysis, noise estimation and reduction, dimension reduction, unmixing, source separation, endmember extraction, segmentation, classification, high performance computing and compression.

Applications oriented papers were also welcome. As a matter of fact, spectrometry is now used in a wide range of domains, including: airborne and satellite remote sensing, monitoring of the environment, pollution, precision agriculture, chemistry, biomedical imagery, defense application, industrial inspection, food safety, astrophysics

WHISPERS is also a place for cross-fertilization between industrial partners and researchers from the academic world. We would like to thank the companies exhibiting their latest products during the event (Specim, NEO, Itres, and Headwall Photonics) or sponsoring the conference (SpecTir, HyVista and SSI). They are the leaders in the field and we were very happy to welcome them in Reykjavik.

Beyond the technical program, whose quality was highly appreciated by all the attendees, the workshop included some nice social events, including an icebreaker reception and a banquet on the island of Videy. After a memorable and joyful evening, the participants could enjoy a very scenic midnight sun on the ferry back to Reykjavik.

Reykjavik is the capital of Iceland and the northernmost capital city in the world, it was founded in 1786. The Reykjavik Capital Area has just under 200,000 inhabitants, about 60% of the total Icelandic population of 300,000. Visitors to Reykjavik experience easily the pure energy at the heart of Iceland’s capital city whether from the boiling thermal energy underground, the natural green energy within the city and around it, or the lively culture and fun-filled nightlife. Many attendees took some extra time to explore other parts of Iceland, including a trip the world famous Geysir.

We would like to thank the members of the program committee for their detailed reviews, which enabled a careful selection, ensuring a high quality workshop. We would also like to thank the organizers of the special sessions: They gathered outstanding contributions. Finally, we would like to thank everyone from the local organizing committee. It has been a wonderful experience working with a great team.

Starting a new series of successful conferences is a very exciting moment. After fruitful WHISPERS meetings in Grenoble, France (2009) and Reykjavik, Iceland (2010), we are very happy to announce that the 2011 WHISPERS will be held in Lisbon, Portugal, June 2011 and hosted by Profs. Jose Bioucas Dias and Antonio Plaza. The usual policy will be used: submission of full 4-pages papers and anonymous peer-review to ensure the optimal quality of the technical contributions.

See you in Lisbon, Portugal, in June 2011 for the GRSS premier event in the hyperspectral world!

Jón Atli Benediktsson, University of Iceland, Reykjavik, Iceland

Jocelyn Chanussot, Grenoble Institute of Technology, France

Björn Waske, University of Bonn, Germany

WHISPERS gathered around 160 attendees from 30 different countries worldwide.

Midnight sun as enjoyed on the ferry back from the banquet.

Our warmest thanks to our three prestigious plenary speakers, Alan Schaum, Xiuping Jia and David Goodenough.

WHISPERS is also a place for cross-fertilization between industrial partners and researchers from the academic world. We would like to thank the industrial exhibitors and sponsors who support the conference.

WHISPERS 2010

2nd Workshop on Hyperspectral Image and Signal Processing – Evolution in Remote Sensing

http://www.ieee-whispers.com

Summary report by Jón Atli Benediktsson, Jocelyn Chanussot and Björn Waske

The Second Workshop on Hyperspectral Image and Signal Processing – Evolution in Remote Sensing (WHISPERS) was held at the campus of the University of Iceland, Reykjavik, Iceland June 14-16, 2010. WHISPERS 2010 received the technical sponsorship of the IEEE Geoscience and Remote Sensing Society (GRSS) and financial sponsorships from the University of Iceland and the IEEE Iceland Section. Organized in two parallel tracks over three days, the workshop was a great success, gathering 160 researchers from 30 different countries worldwide.

A total of 161 papers were submitted to WHISPERS 2010 (both regular submissions and special session submissions), 140 of which were accepted, corresponding to a 13 % rejection rate. Ninety oral presentations organized in 18 sessions were given at the workshop, and 50 posters organized in 6 sessions were presented in interactive sessions. The evaluation of all the papers was performed based on the reports of anonymous reviewers. On average, each paper received 2.5 reviews. All the papers published at WHISPERS 2010 are available on IEEE Xplore.

The technical program also featured three outstanding plenary talks delivered by three prestigious and highly recognized experts:

  • Dr Alan Schaum, from the Naval Research Laboratory, USA, delivered a talk entitled „the Continuum Fusion, a new Theory of Inference.”

  • Dr Xiuping Jia, from the School of Engineering and Information Technology University College, The University of New South Wales, Australian Defence Force Academy, Australia, delivered the talk “Feature Mining from a Hyperspectral Data Cube for Information Mapping: 3D and Beyond.”

  • Dr David G. Goodenough, from the Department of Computer Science, University of Victoria, Canada, delivered the talk “Hyperspectral Applications for Forestry.”

Three papers were selected to receive a Best Paper Award. The authors received one copy of the greatly sought-after “golden whispers” trophy, and a certificate of recognition during a memorable banquet. Congratulations go to:

  • Gabriele Moser and Sebastiano B. Serpico for their outstanding contribution “A Markovian Generalization of Support Vector Machines for Contextual Supervised Classification of Hyperspectral Images”

  • Joel Kuusk and Andres Kuusk for their outstanding contribution “Autonomous Lightweight Airborne Spectrometers for Ground Reflectance Measurements”

  • Iryna Danilina, Alan R. Gillespie, Matthew Smith, Lee Balick and Elsa Abbott for their outstanding contribution “Thermal Infrared Radiosity and Heat Diffusion Model Verification and Validation”.

The aim of the WHISPERS workshop is to bring together all the people involved in spectroscopy and hyperspectral data processing, generally speaking.

By “data“, we mean : signals, as provided by spectrometers and processed individually, images, from the ground using microscopes and spectrometers to airborne or satellite sensors, up to astrophysical data and models: models of the sensors or of the sensed scene, including physical considerations.

By “processing“, we mean everything from the acquisition, the calibration to the analysis. People were invited to submit new research results on the following suggested topics: spectrometers and hyperspectral sensors (design and calibration), physical modeling, physical analysis, noise estimation and reduction, dimension reduction, unmixing, source separation, endmember extraction, segmentation, classification, high performance computing and compression.

Applications oriented papers were also welcome. As a matter of fact, spectrometry is now used in a wide range of domains, including: airborne and satellite remote sensing, monitoring of the environment, pollution, precision agriculture, chemistry, biomedical imagery, defense application, industrial inspection, food safety, astrophysics

WHISPERS is also a place for cross-fertilization between industrial partners and researchers from the academic world. We would like to thank the companies exhibiting their latest products during the event (Specim, NEO, Itres, and Headwall Photonics) or sponsoring the conference (SpecTir, HyVista and SSI). They are the leaders in the field and we were very happy to welcome them in Reykjavik.

Beyond the technical program, whose quality was highly appreciated by all the attendees, the workshop included some nice social events, including an icebreaker reception and a banquet on the island of Videy. After a memorable and joyful evening, the participants could enjoy a very scenic midnight sun on the ferry back to Reykjavik.

Reykjavik is the capital of Iceland and the northernmost capital city in the world, it was founded in 1786. The Reykjavik Capital Area has just under 200,000 inhabitants, about 60% of the total Icelandic population of 300,000. Visitors to Reykjavik experience easily the pure energy at the heart of Iceland’s capital city whether from the boiling thermal energy underground, the natural green energy within the city and around it, or the lively culture and fun-filled nightlife. Many attendees took some extra time to explore other parts of Iceland, including a trip the world famous Geysir.

We would like to thank the members of the program committee for their detailed reviews, which enabled a careful selection, ensuring a high quality workshop. We would also like to thank the organizers of the special sessions: They gathered outstanding contributions. Finally, we would like to thank everyone from the local organizing committee. It has been a wonderful experience working with a great team.

Starting a new series of successful conferences is a very exciting moment. After fruitful WHISPERS meetings in Grenoble, France (2009) and Reykjavik, Iceland (2010), we are very happy to announce that the 2011 WHISPERS will be held in Lisbon, Portugal, June 2011 and hosted by Profs. Jose Bioucas Dias and Antonio Plaza. The usual policy will be used: submission of full 4-pages papers and anonymous peer-review to ensure the optimal quality of the technical contributions.

See you in Lisbon, Portugal, in June 2011 for the GRSS premier event in the hyperspectral world!

Jón Atli Benediktsson, University of Iceland, Reykjavik, Iceland

Jocelyn Chanussot, Grenoble Institute of Technology, France

Björn Waske, University of Bonn, Germany

Caption: The banquet was held on the Island of Videy

Caption: WHISPERS gathered around 160 attendees from 30 different countries worldwide.

Caption: Midnight sun as enjoyed on the ferry back from the banquet.

Caption: Our warmest thanks to our three prestigious plenary speakers, Alan Schaum, Xiuping Jia and David Goodenough.

Caption: WHISPERS is also a place for cross-fertilization between industrial partners and researchers from the academic world. We would like to thank the industrial exhibitors and sponsors who support the conference.

March 31, 2011 – 7:43 pm | By william | Posted in Newsletter, Spotlight on Organizations | Comments (0)

GRSS French Chapter

IEEE Geoscience and Remote Sensing French Chapter

Report presented by Jocelyn Chanussot, GIPSA-Lab, Grenoble Institute of Technology

and Grégoire Mercier, Télécom Bretagne, Brest.

1. History

The French community in geoscience and remote sensing has been very active over the past decades, with a lot of academic research labs, the French space agency (CNES), and numerous industrial partners (Thales, Sagem, Eurocopter, Magellium, CS, CLS etc…).

This community has been deeply involved for years in GRSS activities, and the GRSS French Chapter was finally officially founded in 2007 with the following board:

Jocelyn Chanussot
Grenoble Institute of Technology President

Grégoire Mercier
Telecom Brest Secretary

Didier Massonnet
CNES Toulouse Treasurer

As illustrated in this report, the actions of the Chapter consisted in:

  • promoting geoscience and remote sensing activities among researchers, PhD students and industrial partners, but also under-graduate and graduate students through summer programs.
  • promoting the IEEE GRSS as the leading society in this field,
  • working in collaboration with other existing structures at the national level.

2. Main actions

2.1. Technical meetings

The chapter has every year a number of technical meetings. It can be at the occasion of a PhD dissertation, gathering several members from different cities. It can also be at the occasion of one-day thematic workshops. These workshops have been organized in collaboration with the French GDR ISIS (working group on Information Signal, Images and viSion). In particular, the Chapter was involved in two specific actions:

  • one action on Change Detection
  • on action on Multivariate Image Processing

In average, these workshops are held 2 times every year, gathering around 50 people. Each day starts with a plenary introduction (Jon Atli Benediktsson, Lorenzo Bruzzone, Ridha Touzi have been such guest speakers over the past few years) and a brief presentation of GRSS. These invited talks are then followed by 6 to 8 presentations by researchers or PhD students from all over the country.

2.2. Summer school

In 2008, a summer school on very high resolution remote sensing has been organized in Grenoble. During 5 days, the program included 28 hours of lectures and 6 hours of lab sessions. The lectures were given by prestigious European experts and the event gathered around 60 attendees, both academics (researchers and PhD students) and industrial partners.

2.3. Special issue of Traitement du Signal

In 2009, the Chapter Board edited a special issue of the French Journal “Traitement du Signal”. The topic of the special issue was “remote sensing for the monitoring and the management of the environment” and 6 outstanding contributions were selected, addressing various topics in remote sensing, from the monitoring of glaciers using interferometric SAR data to the counting of birds using high resolution optical data or the monitoring of tropical forests.

2.4. Edited book

In 2010, an Edited Book on “Multivariate Image Processing” is published by ISTE London and Wiley, USA. This book contains a set of chapters written by the participants of the thematic workshops previously described (for instance on change detection, pansharpening, spectral-spatial classifciation in hyperspectral imagery…), most of the times in association with some foreign colleagues in order to increase the potential readership.

2.5. Electronic Newsletter

An informal newsletter is sent to all the French GRSS members anytime some relevant information is available. The table below presents a typical table of content of one electronic newsletter. It includes information about the election of the new board, some call for papers (for conferences and IEEE Journals), one job offer in hyperspectral processing, 2 Master Thesis/PhD offers, a call for participation for the next thematic day (on object recognition) and a (re-)call to include PhD reports on the GRSS website.

Beyond GRSS members, the newsletter is also sent to around 300 people involved in remote sensing. This is also a way to remind everyone of GRSS and stimulate membership (see item 2.6.)

Newsletter aperiodique du Chapitre France IEEE Geoscience and Remote Sensing Society

(sorry for multiple postings !)

  1. Election du nouveau bureau IEEE GRSS, chapitre France
  2. Conference IEEE GRSS Workshop on Hyperspectral Image and Signal Processing (WHISPERS)
  3. Poste CDI imagerie hyperspectrale, societe Actimar
  4. Offre de stage + these, radar, SONDRA/Supélec et SATIE/ENS Cachan
  5. Offre de stage en recalage d’images, EADS
  6. Journee Reconnaissance d’objets en imagerie spatiale du GDR ISIS (debut mai, deadline de proposition: 20 mars)
  7. Numero special “Advances in Remote Sensing Image Processing”, IEEE Journal of Selected Topics in Signal Processing
  8. Numero special “spectral unmixing of remotely sensed data”, IEEE Transactions on Geoscience and Remote Sensing
  9. Vos theses en teledetection sur le site IEEE GRSS
  10. Abonnement / désabonnement / contributions

Table of content of the Electronic Newsletter.

2.5. GRSS PhD Excellence award

In 2009, the French Chapter initiated an European action: a GRSS PhD Excellence award has been established. 5 PhD have been awarded with a good thematic and geographical balance.

T. Peng, who prepared her PhD under the supervision of J. Zerubia received one of these awards in the name of the French Chapter for her thesis on “New higher order active contour models, shape priors, and multiscale analysis, their application to road network extraction from very high resolution satellite images”.

2.6. Statistical features

In order to support the excellence of the French Chapter activities, we provide the following features comparing the number of GRSS members in 2010 and in 2006, right before the Chapter was founded. It shows a significant increase of 60% in terms of membership.

2006 2010 difference
Fellows 0 2 +2
Senior Members 10 13 +3 (+30%)
Members 37 71 +34 (+92%)
Grad. and students 8 7 -1 (-12,5 %)
Affiliate or associate 10 11 +1 (+10%)
TOTAL 65 104 +39 (+60%)

1. Chapter Excellence Award

As a recognition of its activities, the French chapter received the IEEE GRS-S 2010 Chapter Excellence Award, “for excellence as a GRS-S chapter demonstrated by exemplary activities during 2009″. Jocelyn Chanussot and Grégoire Mercier received the IEEE Certificate at IGARSS 2010 in Honolulu, Hawai, at the Awards ceremony held during the banquet. In addition to the Certificate, the award also consists in an honorarium of $1,000 to be used for Chapter activities. This money will be used in 2011 to support the particiation of some PhD students to IEEE GRSS sponsored conferences.

Jocelyn Chanussot, (second from left) and Grégoire Mercier (second from right) receive the Chapter Excellence Award for the French Chapter, with GRS-S President Alberto Moreira (right) and Awards Co-Chair Martti Hallikainen at IGARSS 2010

2. New board and future actions

A living community is a community where everyone can be involved and bring new ideas. Bringing new active and highly motivated members to the board of the chapter is a good way to generate new activities. Consequently, after 3 years, Jocelyn Chanussot stepped down and a new board was elected in 2010 for a 3 years term:

Grégoire Mercier
Telecom Brest President

Roger Fjortoft
CNES Toulouse Secretary

Rodolphe Marion
CEA Paris Treasurer

The GRS-S French Chapter is still taking specific care on information diffusion. A website is under construction and will open in 2011. It will relay information of interest (such as deadlines, call for papers for national and international conferences and workshops) for the French geoscience and remote sensing community. It will also centralize the PhD thesis (written in French) on the field of remote sensing as it is the case on the GRSS website. In this way the aim of the French GRS-S Chapter is to relay activities of the GRS Society down to the national scale.

March 31, 2011 – 6:32 pm | By william | Posted in Community News, Newsletter | Comments (0)

IGARSS 2011 Update

April 14, 2011

Dear Colleague,

You are cordially invited to join us for IGARSS 2011 in Vancouver. Registration for the conference is now open.

The IGARSS 2011 team has worked very hard to make the conference a great success and the technical program for IGARSS 2011 is truly outstanding.

We hope to see you in Vancouver in July.

Jon Atli Benediktsson
President, IEEE GRSS

Motoyuki Sato
General Chair, IGARSS 2011

March 17, 2011 – 9:23 pm | By william | Posted in News | Comments (0)

2011 IEEE-DigitalGlobe Data Fusion Contest!

2011 IEEE-DigitalGlobe Data Fusion Contest!

The Data Fusion Contest has been organized by the Data Fusion Technical Committee and annually proposed since 2006. It is a contest open not only to IEEE members, but to everyone. However, final results will be announced at the 2011 IEEE International Geoscience and Remote Sensing Symposium to be held in Sendai (Japan) in August 2011.

This year the Data Fusion Contest aims at exploiting multi-angular acquisitions over the same target area. Since there are a large variety of possible applications, each participant can decide the research topic to work with.

A set of WorldView-2 multi-sequence images have been provided by DigitalGlobe. This unique data set is composed by five Ortho Ready Standard Level-2 WorldView-2 multi-angular acquisitions, including both 16 bit panchromatic and multi-spectral 8-band images. The imagery was collected over Rio de Janeiro (Brazil) on January 2010 within a three minute time frame. The multi-angular sequence contains the downtown area of Rio, including a number of large buildings, commercial and industrial structures, the airport and a mixture of community parks and private housing.

1. Accessing Imagery:

  • Each participant is required to register in order to gain access to the images. As part of the registration process, each participant is required to accept the DigitalGlobe End User License Agreement.
  • After registration, an email will be sent with the link to the images.

2. Submission Requirements:

  • Papers must be submitted by May 31, 2011 to be considered for this contest. The paper must be in English, and be no more than 4 pages including illustrations and references. The paper should describe in detail the addressed problem, method, result, etc.
  • The paper must be submitted as a pdf document and the title of the file should be the last name of the first author, followed by the first name. If multiple papers will be submitted by the same author, the file name should also include a progressive number (for example, for the second paper submitted by Paul Smith, the file should be named as smithpaul2.pdf).

3. Reviewing process:
All papers will undergo a reviewing process by the Data Fusion Award Committee. The winner(s) will be announced at IGARSS2011 and awarded with an IEEE Certificate of Recognition.

4. Award Committee:

  • Jocelyn Chanussot, Grenoble Institute of Technology, France
  • Jenny Q. Du, Mississippi State University, USA
  • Fabio Pacifici, DigitalGlobe, Inc., USA
  • Paolo Gamba, University of Pavia, Italy
  • Lori Bruce, Mississippi State University, USA

5. Moderated Discussion Forum:
For this contest, we have created a discussion group in LinkedIn to help facilitate an open exchange of ideas. Please join the IEEE GRSS Data Fusion Contest Discussion Forum at:
http://www.linkedin.com/e/3bcwp5-ggcivw2b-40/vgh/3678437/

December 27, 2010 – 2:27 pm | By dev | Posted in News, Opportunities | Comments (0)

Full Professor Position

Full professor position – or tenure track assistant professor
position – in remote sensing and earth surface monitoring

Detailed information

The Faculty of Geosciences and Environment at the University of Lausanne invites
applications for a professor position. We are looking for an internationally recognized scientist in the area of remote sensing and its application to environmental and earthsurface monitoring.

The candidate should have a strong background and experience in quantitative analysis
of active or passive satellite and airborne data. Of particular interest is a focus on
monitoring of earth surface changes, topography, environmental and natural hazards,
urbanization, and land cover change. The candidate should demonstrate scientific
excellence with a strong record of peer reviewed publications on the subjects.

The candidate should possess a strong quantitative background and demonstrate
potential of collaboration with groups that develop quantitative methods supporting
remote sensing data analysis. We expect the successful candidate to pursue a flexible
approach towards new techniques and the development of new methods. Experience in
working on projects or programs with international organizations is an asset.
Appointment is either at full professor level or at assistant professor tenure-track,
possibly leading to full professor within 5-6 years, (an habilitation or HDR is not a
precondition to apply for this position).

Research
An important objective of this position is to develop a strong research group working on
both, fundamental and applied remote sensing.

The candidate is expected to be able to develop an international competitive research program, to supervise and to advise postdocs, PhD, MSc and BSc students, to obtain third party funding, and to create synergies with other faculty members from environmental sciences, earth sciences, geography, and beyond.

Teaching
The candidate must demonstrate excellence in teaching remote sensing methods and
their application in the field of geo- and environmental science. The successful candidate
will contribute to faculty curricula at different levels (BSc, MSc, and PhD).

The candidate should be able to communicate and teach classes in French or English. If
fluency in French is not given, ability to teach in French needs to be acquired within two
years following appointment.


Administration
The successful candidate will participate in administrative tasks within the Institute and
the Faculty, and will serve on committees of the Faculty of Geosciences and
Environment.

General information
The Institute of Geomatics and Analysis of Risk was created in 2004 just after the
creation of the Faculty of Geosciences and Environment [FGSE] of the University of
Lausanne in 2003. The FGSE was created from the merger of three Earth Sciences
institutes and IGUL. In 2004 and 2005 two new institutes (Institute of Geomatics and
Risk Analysis [IGAR], Institute of Land Use Policies and Human Environment [IPTEH])
were added. The Faculty of Geosciences and Environment is a transdisciplinary structure,committed to combining and exploring fundamental skills from natural and human sciences. Its study object is the earth, with a focus on the human being, both as an actor and target within a natural and social environment.

In order to meet its goals in terms of teaching and research, the Faculty is part of a
larger network composed of several institutional partners from the University of
Lausanne and, most notably, from the Universities of Fribourg [UNIFR], Geneva [UNIGE],
and Neuchâtel [UNINE] and the Swiss Federal Institute of Technology, Lausanne [EPFL].
It also works with regional, national or international partners. Within this network of
collaboration, geosciences have a central role. A hallmark of the Faculty’s vitality is the
number of young researchers from different backgrounds who vibrantly develop
innovative research and ideas.

The Faculty of Geosciences and Environment enjoys an exceptionally green lakeside
setting. The Campus is only few minutes away from the center of Lausanne by public
transport. Lausanne is the capital city of the Canton de Vaud. It is renowned for the
quality and the variety of its cultural activities.

Application deadline: January 31, 2011
Starting date: August 1, 2011 (or alternative agreed upon date)

The University of Lausanne is an equal opportunity employer. Applications from women
are particularly encouraged.

Applications are to be submitted both by e-mail and by paper copy to the Dean of the
Faculty of Geosciences and Environment. The application material should include a
motivation letter, a curriculum vitae, a publication list, and a statement of research and
teaching goals and interests (not exceeding 3 pages). It should also include reprints of
the five most significant publications, and names and contact information of five referees.

Further information on the Faculty is available on its website: http ://www.unil.ch/gse.

For specific enquiries, please contact:
Prof. Jean Ruegg (doyen.gse@unil.ch), Dean of the Faculty

The application shall be submitted by e-mail to doyen.gse@unil.ch and via regular mail to
Faculty of Geosciences and Environment, University of Lausanne, to the attention of the
Dean, Bâtiment Amphipôle, CH-1015 Lausanne, Switzerland.

December 21, 2010 – 4:27 pm | By dev | Posted in Job Postings, News, Opportunities | Comments (0)

New Technical Awards

The IEEE Awards Program provides peer recognition to individuals whose contributions to the art and science of electro- and information technologies worldwide have improved the quality of life.

The IEEE Geoscience & Remote Sensing Society members may be particularly interested in the following Technical Field Awards, whose nomination deadlines are 31 January 2011.  The awards typically consist of a bronze medal, certificate and cash honorarium.

Click here for a full description of the new awards.

November 29, 2010 – 8:12 pm | By dev | Posted in Awards, Community News, News, Newsletter | Comments (0)

Wanted: new scientific talent to support EU policy-making

Wanted: new scientific talent to support EU policy-making – The Joint Research Centre is recruiting

The European Commission’s Joint Research Centre (JRC) is a reference centre of science and technology for the European Union. Operating in Belgium, Germany, Italy, Spain and the Netherlands, with a headcount of over 2,700, the JRC is currently seeking researchers with the right blend of competence, experience and language skills to work at the cutting edge of scientific and technological developments supporting EU policies.
If you have a sound track record of research in

  • Chemistry, Biology and Health Sciences
  • Physics
  • Structural Mechanics
  • Quantitative Policy Analysis
  • Spatial Sciences
  • Environmental Sciences
  • Energy Sciences
  • Communication/Information Technology

You can make all the difference within the JRC’s stimulating multicultural environment. In return, you can expect a lifetime of different opportunities, a competitive remuneration package, flexible working arrangements and the chance to become involved in some of the most exciting research initiatives in Europe today.
To learn more, visit www.jrc.ec.europa.eu/competitions and as soon as you are ready to apply, fill in your on-line application at www.eu-careers.eu
Registration closes on 4th November 2010.

References: COM/AD/01/10-COM/AD/16/10

The JRC promotes equal opportunities and non-discriminatory practices in the workplace.

October 11, 2010 – 7:07 pm | By dev | Posted in Job Postings, News, Newsletter, Opportunities | Comments (0)

Special issue of the IEEE Transactions on Geoscience and Remote Sensing on Space Technology

Guest Editors: Maria Petrou, George A Lampropoulos and William J Emery

This special issue recognizes the need for cross-disciplinary collaboration and offers a much needed outlet for publishing inter-disciplinary research. The emphasis of the call is on the inter-dependence of software, applications and sensor technology, as it is clear that one cannot totally disassociate hardware from software, or applications from hardware, because they depend on each other very strongly, particularly for Remote Sensing applications. Limitations on the downloading time of images, of storage and computing capacities on board satellites affect very significantly the software that has to be developed and the applications of remote sensing.

  • The submitted papers should concentrate on Remote Sensing applications of Space Technology, with emphasis on the interaction and interdependence of hardware-software-application. Papers must include a significant original application or demonstration of remote sensing capabilities using the new Space Technology. In particular, papers must include more than just a description of original new technology. They must also apply that technology to a remote sensing application in a substantive way.
  • Publication Schedule
    Call for Submissions: 1 September 2010
    Deadline for Submissions: 1 December 2010
    Special Issue Publication: January 2012
  • All submitted papers will be subject to the standard TGRS reviewing process.
  • Papers should be submitted through the TGRS web page: http://mc.manuscriptcentral.com/tgrs

Instructions for creating new user accounts, if necessary, are available on the login screen. Please indicate during your submission that the paper is intended for this Special Issue by selecting “Space Technology” from the pull down menu for manuscript type. Questions concerning the submission process should be addressed to tgrs-editor@ieee.org. Other questions concerning the Special Issue should be addressed to the Guest Editors:

Maria Petrou
Director of the Informatics and Telematics Institute (ITI), Centre for Research and Technology (CERTH), GREECE
Chair of Signal Processing, Imperial College London, UK
Email: petrou@iti.gr or maria.petrou@imperial.ac.uk

George Lampropoulos
Adjunct Professor of Electrical Engineering, University of Calgary, CANADA
President and CEO, A.U.G. Signals Ltd
Email: lampro@augsignals.com

William J. Emery
Professor of Aerospace Engineering Sciences, University of Colorado, Boulder, USA
Email: William.Emery@colorado.edu

August 20, 2010 – 8:49 am | By dev | Posted in Community News, News, Newsletter, Opportunities, Transactions | Comments (0)