Overview

Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. The coverage includes the physics and mathematical algorithms of SAR images, a comprehensive treatment of MRF-based remote sensing image classification, statistical approaches for improved classification with the remote sensing data, Wiener filter-based method, and other modern approaches and methods of image processing for remotely sensed data. Each chapter explores a technique for dealing with a specific remote sensing problem. The book offers physical insights on the steps for constructing various digital seismic images. The volume examines image modeling, statistical image classifiers, change detection, independent component analysis, vertex component analysis, image fusion for better classification. It explores unique topics such as accuracy assessment and information-theoretic measure of multiband images and many chapters emphasize issues with synthetic aperture radar (SAR) images. Continued development on imaging sensors creates new opportunities and challenges in image processing for remote sensing. Image Processing for Remote Sensing not only presents the most up to date developments of image processing for remote sensing but also suggests to readers the many challenging problems ahead for further study.

Full Product Details

Author:   C. H. Chen
Publisher:   Taylor & Francis Inc
Imprint:   CRC Press Inc
Dimensions:   Width: 17.80cm , Height: 2.50cm , Length: 25.40cm
Weight:   0.885kg
ISBN:  

9781420066647

ISBN 10:   1420066641
Pages:   400
Publication Date:   17 October 2007
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   Awaiting stock  

Table of Contents

Contents Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface, D.L. Schuler, J.-S. Lee, and D. Kasilingam MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation, S.B. Serpico and G. Moser Random Forest Classification of Remote Sensing Data, S.R. Joelsson, J.A. Benediktsson, and J.R. Sveinsson Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning, R. Nishii and S. Eguchi Unsupervised Change Detection in Multi-Temporal SAR Images, L. Bruzzone and F. Bovolo Change-Detection Methods for Location of Mines in SAR Imagery, M. Tates, N. Nasrabadi, H. Kwon, and C. White Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data, J.M.B. Dias and J.M.P. Nascimento Two ICA Approaches for SAR Image Enhancement, C.H. Chen, X. Wang, and S. Chitroub Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery, M. Bertacca, F. Berizzi, and E.D. Mese Spatial Techniques for Image Classification, S. Aksoy Data Fusion for Remote-Sensing Applications, A.H.S. Solberg The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing, B. Escalante-Ramirez and A.A. Lopez-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, A. Stein, G. Gort, and A. Lucieer SAR Image Classification by Support Vector Machine, M. Yoshioka, T. Fujinaka, and S. Omatu Quality Assessment of Remote-Sensing Multi-Band Optical Images, B. Aiazzi, L. Alparone, S. Baronti, and M. Selva Index

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University of Massachusetts, North Dartmouth, USA

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