Remote Sensing with Imaging Radar
J. A. Richards
This book treats the technology of radar imaging for remote sensing applications in a manner suited to the mathematical background of most earth scientists. It assumes no prior knowledge of radar on the part of the reader; instead it commences with a development of the essential concepts of radar before progressing through to a detailed coverage of contemporary ideas such as polarimetry and interferometry.
Because the technology of radar imaging is potentially complex the first chapter provides a framework against which the rest of the book is set. Together, the first four chapters present the technical foundations for remote sensing with imaging radar. Scattering concepts are then covered so that the reader develops the knowledge necessary for interpreting radar data, itself the topic of a later chapter which draws together the current thinking in the analysis of radar imagery.
The treatment is based on the assumption that the radars of interest are, in general, multi-polarised. Polarisation synthesis and polarised interferometric SAR are among the topics covered, as are tomography and the various forms of interferometry. A full chapter is given to bistatic radar, which is now emerging as an imaging technology with enormous potential and flexibility in remote sensing. The book concludes with a summary of passive microwave imaging.
A set of appendices is included that provide supplementary material, among which is an overview of the rather complicated process of image formation with synthetic aperture radar, and summaries of some of the mathematical procedures important for a full appreciation of radar as a remote sensing technology
Statistics for Imaging, Optics, and Photonics
A vivid, hands-on discussion of the statistical methods in imaging, optics, and photonics applications
In the field of imaging science, there is a growing need for students and practitioners to be equipped with the necessary knowledge and tools to carry out quantitative analysis of data. Providing a self-contained approach that is not too heavily statistical in nature, Statistics for Imaging, Optics, and Photonics presents necessary analytical techniques in the context of real examples from various areas within the field, including remote sensing, color science, printing, and astronomy.
Bridging the gap between imaging, optics, photonics, and statistical data analysis, the author uniquely concentrates on statistical inference, providing a wide range of relevant methods. Brief introductions to key probabilistic terms are provided at the beginning of the book in order to present the notation used, followed by discussions on multivariate techniques such as:
- Linear regression models, vector and matrix algebra, and random vectors and matrices
- Multivariate statistical inference, including inferences about both mean vectors and covariance matrices
- Principal components analysis
- Canonical correlation analysis
- Discrimination and classification analysis for two or more populations and spatial smoothing
- Cluster analysis, including similarity and dissimilarity measures and hierarchical and nonhierarchical clustering methods
Intuitive and geometric understanding of concepts is emphasized, and all examples are relatively simple and include background explanations. Computational results and graphs are presented using the freely available R software, and can be replicated by using a variety of software packages. Throughout the book, problem sets and solutions contain partial numerical results, allowing readers to confirm the accuracy of their approach; and a related website features additional resources including the book’s datasets and figures.
Statistics for Imaging, Optics, and Photonics is an excellent book for courses on multivariate statistics for imaging science, optics, and photonics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for professionals working in imaging, optics, and photonics who carry out data analyses in their everyday work.
Remote Sensing Image Processing
This book examines the processes that support earth observation, which can help to mitigate a variety of social and environmental issues, such as devastation caused by natural disasters. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics and communications. All of the applications are tackled under specific formalisms from a machine learning and signal/image processing point of view, such as classification and clustering, regression and function approximation, data coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. Read the full abstract.
Optical Remote Sensing
Optical remote sensing involves acquisition and analysis of optical data – electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground. Optical image acquisition modalities have come a long way – from gray-scale photogrammetric images to hyperspectral images. The advances in imaging hardware over recent decades have enabled availability of high spatial, spectral and temporal resolution imagery to the remote sensing analyst. These advances have created unique challenges for researchers in the remote sensing community working on algorithms for representation, exploitation and analysis of such data.
Early optical remote sensing systems relied on multispectral sensors, which are characterized by a small number of wide spectral bands. Although multispectral sensors are still employed by analysts, in recent years, the remote sensing community has seen a steady shift to hyperspectral sensors, which are characterized by hundreds of fine resolution co-registered spectral bands, as the dominant optical sensing technology. Such data has the potential to reveal the underlying phenomenology as described by spectral characteristics accurately. This “extension” from multispectral to hyperspectral imaging does not imply that the signal processing and exploitation techniques can be simply scaled up to accommodate the extra dimensions in the data. This book presents state-of-the-art signal processing and exploitation algorithms that address three key challenges within the context of modern optical remote sensing: (1) Representation and visualization of high dimensional data for efficient and reliable transmission, storage and interpretation; (2) Statistical pattern classification for robust land-cover-classification, target recognition and pixel unmixing; (3) Fusion of multi-sensor data to effectively exploit multiple sources of information for analysis.
Image Registration for Remote Sensing
Image registration is a digital processing discipline that studies how to bring two or more digital images into precise alignment for analysis and comparison. Accurate registration algorithms are essential for creating mosaics of satellite images and tracking changes on the planet’s surface over time. Bringing together invited contributions from 36 distinguished researchers, the book presents a detailed overview of current research and practice in the application of image registration to remote sensing imagery. Chapters cover the problem definition, theoretical issues in accuracy and efficiency, fundamental algorithms, and real-world case studies of image registration software applied to imagery from operational satellite systems. This book provides a comprehensive and practical overview for Earth and space scientists, presents image processing researchers with a summary of current research, and can be used for specialised graduate courses.
Microwave Radiometer Systems: Design and Analysis, Second Edition
David Le Vine
This practical reference covers radiometer receivers on a block diagram level, helping you determine whether to use direct- or super-heterodyne receivers and describing how to a combine double sideband or single sideband mixer operation with a microwave preamplifier. The book introduces the basic concept of aperture synthesis, explaining the benefits of using it for remote sensing. Moreover, you gain a thorough understanding of synthetic aperture radiometers and are provided with real-world examples, including the ESTAR and HYDROSTAR sensors. This comprehensive book also covers the relationships between swathwidth, footprint, integration time, sensitivity, and frequency for satellite born, real aperture imaging systems.
Neural Networks in Atmospheric Remote Sensing
William J. Blackwell, MIT Lincoln Laboratory
Frederick W. Chen, Signal Systems Corporation
This authoritative reference offers you a comprehensive understanding of the underpinnings and practical applications of artificial neural networks and their use in the retrieval of geophysical parameters. You find expert guidance on the development and evaluation of neural network algorithms that process data from a new generation of hyperspectral sensors. The book provides clear explanations of the mathematical and physical foundations of remote sensing systems, including radiative transfer and propagation theory, sensor technologies, and inversion and estimation approaches. You discover how to use neural networks to approximate remote sensing inverse functions with emphasis on model selection, preprocessing, initialization, training, and performance evaluation.
Statistical Image Processing and Multidimensional Modeling
Signal Theory Methods in Multispectral Remote Sensing
David A Landgrebe
An outgrowth of the author’s extensive experience teaching senior and graduate level students, this is both a thorough introduction and a solid professional reference.
Material covered has been developed based on a 35-year research program associated with such systems as the Landsat satellite program and later satellite and aircraft programs.
Covers existing aircraft and satellite programs and several future programs
An Instructor’s Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Radiowave Propagation: Physics and Applications
Joel T. Johnson
Fernando L. Teixeira
Propagation-the process whereby a signal is conveyed between transmitter and receiver-has a profound influence on communication systems design. Radiowave Propagation provides an overview of the physical mechanisms that govern electromagnetic wave propagation in the Earth’s troposphere and ionosphere. Developed in conjunction with a graduate-level wave propagation course at The Ohio State University, this text offers a balance of physical and empirical models to provide basic physical insight as well as practical methods for system design.
Beginning with discussions of propagation media properties, plane waves, and antenna and system concepts, successive chapters consider the most important wave propagation mechanisms for frequencies ranging from LF up to the millimeter wave range, including:
- Direct line-of-sight propagation through the atmosphere
- Rain attenuation
- The basic theory of reflection and refraction at material interfaces and in the Earth’s atmosphere
- Reflection, refraction, and diffraction analysis in microwave link design for a specified terrain profile
- Empirical path loss models for point-to-point ground links
- Statistical fading models
- Standard techniques for prediction of ground wave propagation
- Ionospheric propagation, with emphasis on the skywave mechanism at MF and HF and on ionospheric perturbations for Earth-space links at VHF and higher frequencies
- A survey of other propagation mechanisms, including tropospheric scatter, meteor scatter, and propagation effects on GPS systems
Radiowave Propagation incorporates fundamental materials to help senior undergraduate and graduate engineering students review and strengthen electromagnetic physics skills as well as the most current empirical methods recommended by the International Telecommunication Union. This book can also serve as a valuable teaching and reference text for engineers working with wireless communication, radar, or remote sensing systems.