Signal and image processing for remote sensing /
edited by C.H. Chen.
2nd ed.
Boca Raton : CRC Press, c2012.
xix, 567 p., [32] p. of plates : ill. (some col.) ; 27 cm.
143985596X (hardcover : alk. paper), 9781439855966 (hardcover : alk. paper)
More Details
Boca Raton : CRC Press, c2012.
143985596X (hardcover : alk. paper)
9781439855966 (hardcover : alk. paper)
catalogue key
Includes bibliographical references and index.
A Look Inside
Review Quotes
Praise for the First Edition ...this book will be useful to advance automated image processing and the integration of remote sensor data with ecosystem and atmospheric models. The unique idea of combining signal processing with image processing is a good one and is well timed with ongoing technological advancements. -Ross Lunetta, co-editor of Remote Sensing Change Detectionand Remote Sensing and GIS Accuracy Assessment Overall, the breadth and depth of content make this book an excellent reference for researchers, including graduate students, engaged in advanced remote sensing data analysis, who will find that some chapters provide inspiration to their own research. -Qian Du, Department of Electrical and Computer Engineering, Mississippi State University, in Photogrammetric Engineering & Remote Sensing, Nov. 2007, Vol. 73, No. 11
This item was reviewed in:
Reference & Research Book News, June 2012
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Main Description
Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Editionexplores the most up-to-date signal and image processing methods for dealing with remote sensing problems. Although most data from satellites are in image form, signal processing can contribute significantly in extracting information from remotely sensed waveforms or time series data. This book combines both, providing a unique balance between the role of signal processing and image processing. Featuring contributions from worldwide experts, this book continues to emphasize mathematical approaches. Not limited to satellite data, it also considers signals and images from hydroacoustic, seismic, microwave, and other sensors. Chapters cover important topics in signal and image processing and discuss techniques for dealing with remote sensing problems. Each chapter offers an introduction to the topic before delving into research results, making the book accessible to a broad audience. This second edition reflects the considerable advances that have occurred in the field, with 23 of 27 chapters being new or entirely rewritten. Coverage includes new mathematical developments such as compressive sensing, empirical mode decomposition, and sparse representation, as well as new component analysis methods such as non-negative matrix and tensor factorization. The book also presents new experimental results on SAR and hyperspectral image processing. The emphasis is on mathematical techniques that will far outlast the rapidly changing sensor, software, and hardware technologies. Written for industrial and academic researchers and graduate students alike, this book helps readers connect the "dots" in image and signal processing. New in This Edition The second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New topics covered include: Compressive sensing The mixed pixel problem with hyperspectral images Hyperspectral image (HSI) target detection and classification based on sparse representation An ISAR technique for refocusing moving targets in SAR images Empirical mode decomposition for signal processing Feature extraction for classification of remote sensing signals and images Active learning methods in classification of remote sensing images Signal subspace identification of hyperspectral data Wavelet-based multi/hyperspectral image restoration and fusion The second edition is not intended to replace the first edition entirely and readers are encouraged to read both editions of the book for a more complete picture of signal and image processing in remote sensing. See Signal and Image Processing for Remote Sensing(CRC Press 2006).
Main Description
Written by more than 50 world leaders in the field, this book covers major topics in signal and image processing for remote sensing. The second edition features new chapters on compressive sensing, the super-resolution method in the mixed pixel problem with hyperspectral images, sparse representation for target detection and classification in hyperspectral imagery, SAR image processing from autofocusing to change detection, and a critical review of pansharpening. Additional topics new to this edition include non-negative matrix and tensor factorization, ISAR imaging of targets, and applications of the Huang-Hilbert transform. The text presents a unique signal-processing point of view for image processing.
Main Description
Written by leaders in the field, Signal Processing for Remote Sensingexplores the data acquisitions segment of remote sensing. Each chapter presents a major research result or the most up to date development of a topic. The book includes a chapter by Dr. Norden Huang, inventor of the Huang-Hilbert transform who, along with and Dr. Steven Long discusses the application of the transform to remote sensing problems. It also contains a chapter by Dr. Enders A. Robinson, who has made major contributions to seismic signal processing for over half a century, on the basic problem of constructing seismic images by ray tracing. With rapid technological advances in both sensor and processing technologies, a book can only capture the current process and result. However, the numerous mathematical techniques provided in this book have lasting value, giving it a useful role for many years to come. While the majority of remote sensing titles cover only image processing, this book focuses on the dataacquisitions segment of remote sensing. Its uniquely specific and practical approach allows you to directly apply the knowledge in this book to your field of remote sensing applications.
Bowker Data Service Summary
Highlighting the data acquisitions segment of remote sensing, this book covers normalized Hilbert transforms and its applications, statistical pattern recognition, neural network-based infrasound event classifier, the seismic data decomposition, and Kalman filtering for weak signal detection in remote sensing.
Bowker Data Service Summary
Highlighting the data acquisitions segment of remote sensing, this book covers Normalized Hilbert Transfroms and its applications, Statistical Pattern Recognition, Neural Network-Based Infrasound Event Classifier, the Siesmic Data Decomposition, and Kalman Filtering for Weak Signal Detection in Remote Sensing.

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