GRSS | IEEE | Geoscience & Remote Sensing Society

Ohio State University ElectroScience Laboratory Announces 2012 Short Courses, August 8-10

william — Fri, 04 May 2012 02:10:12 +0000

The ElectroScience Laboratory will offer nine short courses, August 8-10, 2012, on key topics of interest in antenna design and measurement, computational electromagnetics, flexible RF electronics and wideband front-ends, MIMO, radar and remote sensing. Learn from renowned instructors in a small group environment and attend multiple courses in the same location during this three-day event.

The courses are designed for engineers, technicians, graduate students and others interested in learning about these specialized topics.

2012 Courses

High Resolution Radar and Target Classification by Chris Baker
Design and Operation of UWB Antennas by Chi-Chih Chen
Flexible RF Electronics and Wideband Front-Ends for Wireless Applications by John Volakis and Waleed Khalil
Time Domain Computational Electromagnetics by Fernando Teixeira
Antenna/RCS Measurements and Related Data Processing by Inder “Jiti” Gupta and Teh-Hong Lee
Passive Bistatic/Multistatic Radar by Chris Baker
Ground Penetrating Radar Fundamentals by Chi-Chih Chen
MIMO in Communication and Radar Systems by Jack Winters
Scattering from Statistically Described Rough Surfaces: State of the Art by Joel Johnson

Instruction for the courses is provided by world-class faculty from The Ohio State University’s Department of Electrical and Computer Engineering and the ElectroScience Laboratory.

Can’t come to Columbus? Attend and interact remotely

Attend a course live in Columbus, Ohio, or avoid the cost of traveling and attend and interact remotely via streaming video.

Registration for the short courses will open in May. The 2012 ESL Short Courses flyer (PDF) is available online.

Call for Papers for the EUSAR-2012 Special Issue of TGRS

william — Thu, 03 May 2012 15:43:47 +0000

This Special Issue is associated with the 9th European Conference on Synthetic
Aperture Radar (EUSAR), which was in Nürnberg, Germany in April 2012
(http: //www.eusar.de). EUSAR is the international conference worldwide entirely
dedicated to the technology, techniques development, and applications of Synthetic
Aperture Radar (SAR) for remote sensing. Over the past years EUSAR has provided
an excellent forum for exchanging information and discussion on a wide variety of
SAR topics, representing the latest SAR developments, and has established an
international community of SAR engineers and scientists.

The objective of the Special Issue, open to all researchers, is to select outstanding
contributions on recent advances in the field of SAR, bringing together participants
from the research, industrial and academic communities who are engaged in projects
on the technologies and techniques of SAR.
Contributions for this special issue are welcome on the following topics: SAR and
ISAR systems and sensors, innovative SAR concepts and applications, advanced
SAR modes (ScanSAR, Spotlight, Squint, Bistatic) and their signal processing, very
low frequency SAR systems, bistatic and multistatic SAR systems, passive SAR
systems, multimode and reconfigurable SAR systems, multi-satellite and small
satellite SAR systems, sparse aperture SAR, ultra wide bandwidth and high
resolution SAR, new SAR antenna concepts, SAR signal processing, motion
compensation and geocoding, SAR data evaluation and handling, along and acrosstrack
interferometry, polarimetry and polarimetric interferometry, and moving target
detection, STAP and change detection.
Paper submission deadline: 30 June 2012
Submission guidelines
Prospective authors should follow the regular guidelines of TGRS, and should submit
their manuscripts electronically to http://mc.manuscriptcentral.com/tgrs. Please
indicate during your submission that the paper is intended for this Special Issue.
Inquiries with respect to the special issue should be directed to the Guest Editors
(matthias.weiss@fhr. fraunhofer.de).

Click here for additional information.

IEEE Transactions on Geoscience and Remote Sensing institutional listings

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

Seismic Monitoring of the North Korea Nuclear Test Site Using a Multichannel Correlation Detector

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

North Korea announced a second nuclear test on 25 May 2009, the first having taken place on October 9, 2006. Both tests were detected by the global seismic network of the Comprehensive nuclear Test-Ban-Treaty Organisation. We apply a correlation detector using a 10-s signal template from the 2006 test on the MJAR array in Japan to: 1) assess the potential for automatically detecting subsequent explosions at or near the test site; and 2) monitor the associated false alarm rate. The 2009 signal is detected clearly with no false alarms in a three-year period. By detecting scaled-down copies of the explosion signals submerged into background noise, we argue that a significantly smaller explosion at the site would have been detected automatically, with a low false alarm rate. The performance of the correlator on MJAR is not diminished by the signal incoherence that makes conventional array processing problematic at this array. We demonstrate that false alarm elimination by f-k analysis of single channel detection statistic traces is crucial for maintaining a low detection threshold. Correlation detectors are to be advocated as a routine complement to the existing pipeline detectors, both for reducing the detection threshold for sites of interest and providing automatic classification of signals from repeating sources.

2012 IEEE membership form

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

GPR Full-Waveform Sensitivity and Resolution Analysis Using an FDTD Adjoint Method

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

Radar tomography is a useful technique for mapping the permittivity and conductivity distributions in the shallow subsurface. By exploiting the full radar waveforms, it is possible to improve resolution and, thus, image subwavelength features not resolvable using ray-based approaches. Usually, mere convergence in the data space is the only criterion used to appraise the goodness of a final result, possibly limiting the reliability of the inversion. A better indication of the correctness of an inverted model and its various parts could be obtained by means of a formal model resolution and information content analysis. We present a novel method for computing the sensitivity functions (Jacobian matrix) based on a time-domain adjoint method. Because the new scheme only computes the sensitivity values for the transmitter and receiver combinations that are used, it reduces the number of forward runs with respect to standard brute-force or other virtual-source schemes. The procedure has been implemented by using a standard finite-difference time-domain modeling method. A comparison between cumulative sensitivity (column sum of absolute values of the Jacobian) images, which is sometimes used in geoelectrical studies as a proxy for resolution in practical cases, and formal model resolution images is also presented. We show that the cumulative sensitivity supplies some valuable information about the image, but when possible, formal resolution analyses should be performed. The eigenvalue spectrum of the pseudo-Hessian matrix provides a measure of the information content of an experiment and shows the extent of the unresolved model space.

Sensitivity of Satellite-Derived Wind Retrieval Over Cloudy Scenes to Target Selection in Tracking and Pixel Selection in Height Assignment

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

Satellite-derived atmospheric motion vectors (AMVs) are useful in weather analyses such as for identifying tropical lows, wind shears, and jet locations. AMVs are assimilated into numerical weather prediction models, particularly for ocean areas where wind observations are sparse. An AMV's accuracy is closely related to the processes of target tracking and height assignment (HA). The objective of this paper is to investigate the sensitivity of satellite-derived wind retrieval in cloudy scenes to the main components in these processes. AMVs are retrieved by identifying and tracking targets using advanced pattern-matching techniques based on cross-correlation statistics. In tracking targets, the main components of the AMV algorithm are the target selection methods such as the target box size, the grid size, the time interval between satellite images, and the method for determining the locations of targets. This study reveals that the optimal sizes of the target and grid could be determined differently according to the channel used for wind observation. The time interval between satellite images has a significant impact on the number of vectors with high quality and high accuracy. The HA method is also an important factor in determining the AMVs' accuracy. The heights of most vectors are assigned to cloud-top pressures using the representative radiances, and the current algorithm uses the coldest pixels to set these representative radiances. The template image used for feature tracking may contain various clouds with different movements and different heights. Therefore, without any information on feature tracking, the current approach may lead to HA errors. To mitigate these HA errors, a new approach using the individual-pixel contribution rate is tested. It tends to correct the heights of the AMVs using the water vapor channel and reduces the wind speed bias and root-mean-square vector difference.

Multidimensional Disaggregation of Land Surface Temperature Using High-Resolution Red, Near-Infrared, Shortwave-Infrared, and Microwave-L Bands

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

Land surface temperature data are rarely available at high temporal and spatial resolutions at the same locations. To fill this gap, the low spatial resolution data can be disaggregated at high temporal frequency using empirical relationships between remotely sensed temperature and fractional green (photosynthetically active) and senescent vegetation covers. In this paper, a new disaggregation methodology is developed by physically linking remotely sensed surface temperature to fractional green and senescent vegetation covers using a radiative transfer equation. Moreover, the methodology is implemented with two additional factors related to the energy budget of irrigated areas, being the fraction of open water and soil evaporative efficiency (ratio of actual to potential soil evaporation). The approach is tested over a 5 km by 32 km irrigated agricultural area in Australia using airborne Polarimetric L-band Multibeam Radiometer brightness temperature and spaceborne Advanced Scanning Thermal Emission and Reflection radiometer (ASTER) multispectral data. Fractional green vegetation cover, fractional senescent vegetation cover, fractional open water, and soil evaporative efficiency are derived from red, near-infrared, shortwave-infrared, and microwave-L band data. Low-resolution land surface temperature is simulated by aggregating ASTER land surface temperature to 1-km resolution, and the disaggregated temperature is verified against the high-resolution ASTER temperature data initially used in the aggregation process. The error in disaggregated temperature is successively reduced from 1.65°C to 1.16°C by including each of the four parameters. The correlation coefficient and slope between the disaggregated and ASTER temperatures are improved from 0.79 to 0.89 and from 0.63 to 0.88, respectively. Moreover, the radiative transfer equation allows quantification of the impact on disaggregation of the temperature at high resolution for each parameter: fracti- nal green vegetation cover is responsible for 42% of the variability in disaggregated temperature, fractional senescent vegetation cover for 11%, fractional open water for 20%, and soil evaporative efficiency for 27%.

Radarsat-2 DSM Generation With New Hybrid, Deterministic, and Empirical Geometric Modeling Without GCP

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

Digital surface models (DSMs) extracted from high-resolution Radarsat-2 stereo-images using different geometric modeling (deterministic, new hybrid, and empirical) are evaluated. The 3-D deterministic models are Toutin's and hybrid Toutin's models (TM and HTM) developed at the Canada Centre for Remote Sensing, and the empirical model is the rational function model (RFM). TM is computed with one and eight ground control points (GCPs), HTM without GCP and RFM supplied by MacDonald, Dettwiler and Associates Ltd. is postprocessed with 3-9 GCPs depending of degrees of 2-D polynomial functions. The DSMs are then generated and compared to 0.2-m accurate lidar elevation data. Because DSMs included the height of land covers, elevation linear errors with 68% and 90% confidence level (LE68 and LE90) are computed and compared over bare surfaces only. LE90 results are: TM with eight GCPs achieves the best results (6.3 m), then HTM with no GCP (7 m), TM with one GCP (8.6 m), and finally RFM the worst (9.7 m) whatever the polynomial degree and GCP number. HTM is the only modeling not using any GCP, which offers a strong advantage in operational environments.

Soil Moisture Retrieval Using Time-Series Radar Observations Over Bare Surfaces

Geoscience and Remote Sensing, IEEE Transactions on - new TOC — Tue, 01 May 2012 00:00:00 +0000

A time-series algorithm is proposed to retrieve bare surface soil moisture and rms height using two copolarized (HH and VV) L-band backscattering coefficients (σ⁰). The retrieval approach inverts a forward model for radar scattering from an isotropic bare surface. Because real-time inversion of a complex forward model is often computationally impractical, the inversion is implemented using a precomputed lookup table representation of σ⁰ obtained from numerical Maxwell model in 3-D simulations. The retrieval process assumes that surface roughness properties are constant during the time-series interval, so that only a single rms height estimate is produced for the entire time series. The use of this rms height estimate as a constraint simplifies the associated soil moisture retrievals at each time step. A Monte-Carlo simulation of this algorithm with 0.7 dB radar measurement error (1-sigma) shows that retrievals using six time steps outperform a “snapshot” method (which retrieves rms height and soil moisture at each time step) by a factor of about two in rms soil moisture error. A second study using measured data having 6 to 11 time steps shows an rms error of 0.044 cm³/cm³ for soil moisture with a correlation coefficient of 0.89 between retrieved and in situ data. Surface rms height estimates are also found accurate to 10 to 30% of in situ measurements. It is also shown that retrieval performance is not sensitive to errors in knowledge of the surface roughness correlation length for most of the bare surface conditions examined.