Chen C.H. (ed.) Image Processing for Remote Sensing

CRC Press, 2008. — 418 p.This volume is a spin-off edition derived from Signal and Image Processing for Remote Sensing. It presents more advanced topics of image processing in remote sensing than similar books in the area. The topics of image modeling, statistical image classifiers, change detection, independent component analysis, vertex component analysis, image fusion for better classification or segmentation, 2-D time series modeling, neural network classifications, etc. are examined in this volume. Some unique topics like accuracy assessment and information-theoretic measure of multiband images are presented. An emphasis is placed on the issues with synthetic aperture radar (SAR) images in many chapters. Continued development on imaging sensors always presents new opportunities and challenges on image processing for remote sensing. The hyperspectral imaging sensor is a good example here. We believe this volume not only presents the most upto- date developments of image processing for remote sensing but also suggests to readers the many challenging problems ahead for further study.
Original Preface from Signal and Image Processing for Remote Sensing Both signal processing and image processing have been playing increasingly important roles in remote sensing. While most data from satellites are in image forms and thus image processing has been used most often, signal processing can contribute significantly in extracting information from the remotely sensed waveforms or time series data. In contrast to other books in this field which deal almost exclusively with the image processing for remote sensing, this book provides a good balance between the roles of signal processing and image processing in remote sensing. The book covers mainly methodologies of signal processing and image processing in remote sensing. Emphasis is thus placed on the mathematical techniques which we believe will be less changed as compared to sensor, software and hardware technologies. Furthermore, the term ‘‘remote sensing’’ is not limited to the problems with data from satellite sensors. Other sensors which acquire data remotely are also considered. Thus another unique feature of the book is the coverage of a broader scope of the remote sensing information processing problems than any other book in the area.Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface
MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation
Random Forest Classification of Remote Sensing Data
Supervising Image Classification of Multi-Spectral Images Based on Statistical Machine Learning
Unsupervised Change Detection in Multi-Temporal SAR Images
Change-Detection Methods for Location of Mines in SAR Imagery
Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data
Two ICA Approaches for SAR Image Enhancement
Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery
Spatial Techniques for Image Classification
Data Fusion for Remote-Sensing Applications
The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing
Multi-Sensor Approach to Automated Classificaton of Sea Ice Image Data
Use of the Bradley-Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image
SAR Image Classification by Support Vector Machine
Quality Assessment of Remote-Sensing Multi-Band Optical Images