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Signal and Image Processing for Remote Sensing

Технические >> Электроника





Разместил: bennyblanko

26-04-2015, 12:18

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Signal and Image Processing for Remote Sensing
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.

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:
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 sensrs

Contents :

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
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


Название: Signal and Image Processing for Remote Sensing
Авторы: Chen C.H.
Издательство: N.-Y.: CRC Press
Год выпуска: 2012
Формат: PDF
Страниц: 580
Язык: Английский
Размер: 27,6 Мб


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