Signal and Image Processing for Remote Sensing by C.H. Chen, Chen Chen | | 9780849350917 | Hardcover | Barnes & Noble
Signal and Image Processing for Remote Sensing

Signal and Image Processing for Remote Sensing

by C.H. Chen, Chen Chen
     
 

ISBN-10: 0849350913

ISBN-13: 9780849350917

Pub. Date: 09/14/2006

Publisher: Taylor & Francis

Most data from satellites are in image form, thus most books in the remote sensing field deal exclusively with image processing. However, signal processing can contribute significantly in extracting information from the remotely sensed waveforms or time series data. Pioneering the combination of the two processes, Signal and Image Processing for Remote

Overview

Most data from satellites are in image form, thus most books in the remote sensing field deal exclusively with image processing. However, signal processing can contribute significantly in extracting information from the remotely sensed waveforms or time series data. Pioneering the combination of the two processes, Signal and Image Processing for Remote Sensing provides a balance between the role of signal processing and image processing in remote sensing.

Featuring contributions from worldwide experts, this book emphasizes mathematical approaches. Divided into two parts, Part I examines signal processing for remote sensing and Part II explores image processing. Not limited to the problems with data from satellite sensors, the book considers other sensors which acquire data remotely, including signals and images from infrasound, seismic, microwave, and satellite sensors. It covers a broader scope of issues in remote sensing information processing than other books in this area.

With rapid technological advances, the mathematical techniques provided will far outlast the sensor, software and hardware technologies. Focusing on methodologies of signal processing and image processing in remote sensing, this book discusses unique techniques for dealing with remote sensing problems.

Product Details

ISBN-13:
9780849350917
Publisher:
Taylor & Francis
Publication date:
09/14/2006
Pages:
672
Product dimensions:
7.00(w) x 10.10(h) x 1.50(d)

Table of Contents

SIGNAL PROCESSING FOR REMOTE SENSING

On the Normalized Hilbert Transform and Its Applications in Remote Sensing; Steven R. Long and Norden E. Huang

Statistical Pattern Recognition and Signal Processing in Remote Sensing; Chi Hau Chen

A Universal Neural Network-Based Infrasound Event Classifier; Fredric M. Ham and Ranjan Acharyya

Construction of Seismic Images by Ray Tracing; Enders A. Robinson

Multi-Dimensional Seismic Data Decomposition by Higher Order SVD and Unimodal ICA; Nicholas Le Bihan, Valeriu Vrabie, and Jérôme I. Mars

Application of Factor Analysis in Seismic Profiling; Zhenhai Wang and Chi Hau Chen

Kalman Filtering for Weak Signal Detection in Remote Sensing; Stacy L. Tantum, Yingyi Tan, and Leslie M. Collins

Relating Time-Series of Meteorological and Remote Sensing Indices to Monitor Vegetation Moisture Dynamics; J. Verbesselt, P. Jönsson, S. Lhermitte, I. Jonckheere, J. van Aardt, and P. Coppin

Use of a Prediction-Error Filter in Merging Highand Low-Resolution Images; Sang-Ho Yun and Howard Zebker

Blind Separation of Convolutive Mixtures for Canceling Active Sonar Reverberation; Fengyu Cong, Chi Hau Chen, Shaoling Ji, Peng Jia, and Xizhi Shi

Neural Network Retrievals of Atmospheric Temperature and Moisture Profiles from High-Resolution Infrared and Microwave Sounding Data; William J. Blackwell

Satellite-Based Precipitation Retrieval Using Neural Networks, Principal Component Analysis, and Image Sharpening; Frederick W. Chen

IMAGE PROCESSING FOR REMOTE SENSING

Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface; Dale L. Schuler, Jong-Sen Lee, and Dayalan Kasilingam

MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation; Sebastiano B. Serpico and Gabriele Moser

Random Forest Classification of Remote Sensing Data; Sveinn R. Joelsson, Jon Atli Benediktsson, and Johannes R. Sveinsson

Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning; Ryuei Nishii and Shinto Eguchi

Unsupervised Change Detection in Multi-Temporal SAR Images; Lorenzo Bruzzone and Francesca Bovolo

Change-Detection Methods for Location of Mines in SAR Imagery; Maria Tates, Nasser Nasrabadi, Heesung Kwon, and Carl White

Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data; José M.B. Dias and José M.P. Nascimento

Two ICA Approaches for SAR Image Enhancement; Chi Hau Chen, Xianju Wang, and Salim Chitroub

Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery; Massimo Bertacca, Fabrizio Berizzi, and Enzo Dalle Mese

Spatial Techniques for Image Classification; Selim Aksoy

Data Fusion for Remote-Sensing Applications; Anne H.S. Solberg

The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing; Boris Escalante-Ramírez and Alejandra A. López-Caloca

Multi-Sensor Approach to Automated Classification of Sea Ice Image Data; A.V. Bogdanov, S. Sandven, O.M. Johannessen, V.Yu. Alexandrov, and L.P. Bobylev

Use of the Bradley-Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image; Alfred Stein, Gerrit Gort, and Arko Lucieer

SAR Image Classification by Support Vector Machine; Michifumi Yoshioka, Toru Fujinaka, and Sigeru Omatu

Quality Assessment of Remote Sensing Multi-Band Optical Images; Bruno Aiazzi, Luciano Alparone, Stefano Baronti, and Massimo Selva

Index

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