Signal and Image Processing for Remote Sensing (Signal and Image Processing of Earth Observations)

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores 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.\nFeaturing 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.\nThis 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.\nThe 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.\nNew in This Edition\nThe second edition includes four chapters from the first edition, plus 23 new or entirely rewritten chapters, and 190 new figures. New

Computers & Technology

Chen, C.H.

Computer Science

CRC-Press

Computer-Aided Seismic Analysis and Discrimination.

Science & Math

Earth Sciences

Amsterdam: Elsevier 1978. (Methods in Geochemistry and Geophysics)

Seismic Signal Analysis and Discrimination III (METHODS IN GEOCHEMISTRY AND GEOPHYSICS)

Elsevier Science Ltd.

Statistical pattern recognition

Hayden Book Co.

Written by leaders in the field, Signal Processing for Remote Sensing explores 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 data acquisitions 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.

Signal Processing for Remote Sensing

Avoiding heavy mathematics and lengthy programming details, Digital Image Processing: An Algorithmic Approach with MATLAB® presents an easy methodology for learning the fundamentals of image processing. The book applies the algorithms using MATLAB®, without bogging down students with syntactical and debugging issues. One chapter can typically be completed per week, with each chapter divided into three sections. The first section presents theoretical topics in a very simple and basic style with generic language and mathematics. The second section explains the theoretical concepts using flowcharts to streamline the concepts and to form a foundation for students to code in any programming language. The final section supplies MATLAB codes for reproducing the figures presented in the chapter. Programming-based exercises at the end of each chapter facilitate the learning of underlying concepts through practice. This textbook equips undergraduate students in computer engineering and science with an essential understanding of digital image processing. It will also help them comprehend more advanced topics and sophisticated mathematical material in later courses. A color insert is included in the text while various instructor resources are available on the author’s website.

Digital Image Processing: An Algorithmic Approach with MATLAB (Chapman & Hall/CRC Textbooks in Computing)

Qidwai, Uvais

Chapman and Hall/CRC

Continuing in the footsteps of the pioneering first edition, Signal and Image Processing for Remote Sensing, Second Edition explores 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: <ul>
<li>Compressive sensing</li> <li>The mixed pixel problem with hyperspectral images</li> <li>Hyperspectral image (HSI) target detection and classification based on sparse representation</li> <li>An ISAR technique for refocusing moving targets in SAR images</li> <li>Empirical mode decomposition for signal processing</li> <li>Feature extraction for classification of remote sensing signals and images</li> <li>Active learning methods in classification of remote sensing images</li> <li>Signal subspace identification of hyperspectral data</li> <li>Wavelet-based multi/hyperspectral image restoration and fusion</li> </ul>
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).

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