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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 Signal and Image Processing for Remote Sensing 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.
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.
Signal Processing for Remote Sensing
- On the Normalized Hilbert Transform and Its Applications to Remote Sensing
- Nyquist Pulse-Based Empirical Mode Decomposition and Its Application to Remote Sensing Problems
- Hydroacoustic Signal Classification Using Support Vector Machines
- Huygens Construction and the Doppler Effect in Remote Detection
- Compressed Remote Sensing
- Context-Dependent Classification: An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions
- NMF and NTF for Sea Ice SAR Feature Extraction and Classification
- Relating Time Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics
- Use of a Prediction-Error Filter in Merging High- and Low-Resolution Images
- Hyperspectral Microwave Atmospheric Sounding Using Neural Networks
- Satellite Passive Millimeter-Wave Retrieval of Global Precipitation
Image Processing for Remote Sensing
- On SAR Image Processing: From Focusing to Target Recognition
- Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface
- An ISAR Technique for Refocussing Moving Targets in SAR Images
- Active Learning Methods in Classification of Remote Sensing Images
- Crater Detection Based on Marked Point Processes
- Probability Density Function Estimation for Classification of High-Resolution SAR Images
- Random Forest Classification of Remote Sensing Data
- Sparse Representation for Target Detection and Classification in Hyperspectral Imagery
- Integration of Full and Mixed Pixel Techniques to Obtain Thematic Maps with a Refined Resolution
- Signal Subspace Identification in Hyperspecral Imagery
- Image Classification and Object Detection Using Spatial Contextual Constraints
- Data Fusion for Remote-Sensing Applications
- Image Fusion in Remote Sensing with the Steered Hermite Transform
- Wavelet-Based Multi/Hyperspectral Image Restoration and Fusion
- The Land Cover Estimation with Satellite Image Using Neural Network
- Twenty-Five Years of Pansharpening: A Critical Review and New Developments
Chi Hau Chen is currently the Chancellor Professor Emeritus of electrical and computer engineering at the University of Massachusetts Dartmouth, where he has taught since 1968. Dr. Chen has published 29 books in his areas of research. He served as associate editor of the IEEE Transactions on Acoustics, Speech and Signal Processing for four years, associate editor of the IEEE Transactions on Geoscience and Remote Sensing for 15 years, and since 2008 has been a board member of Pattern Recognition. Dr. Chen is a Life Fellow of the IEEE, a Fellow of the International Association of Pattern Recognition (IAPR), and a member of Academia NDT International.
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 Detection and 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