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List of contents
1. Polarimetric SAR Techniques for Remote Sensing of the Ocean Surface 2. MRF-Based Remote-Sensing Image Classification with Automatic Model Parameter Estimation 3. Random Forest Classification of Remote Sensing Data 4. Supervised Image Classification of Multi-Spectral Images Based on Statistical Machine Learning 5. Unsupervised Change Detection in Multi-Temporal SAR Images 6. Change-Detection Methods for Location of Mines in SAR Imagery 7. Vertex Component Analysis: A Geometric-Based Approach to Unmix Hyperspectral Data 8. Two ICA Approaches for SAR Image Enhancement 9. Long-Range Dependence Models for the Analysis and Discrimination of Sea-Surface Anomalies in Sea SAR Imagery 10. Spatial Techniques for Image Classification 11. Data Fusion for Remote-Sensing Applications 12. The Hermite Transform: An Efficient Tool for Noise Reduction and Image Fusion in Remote-Sensing 13. Multi-Sensor Approach to Automated Classification of Sea Ice Image Data 14. Use of the Bradley�Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image 15. SAR Image Classification by Support Vector Machine 16. Quality Assessment of Remote-Sensing Multi-Band Optical Images
Summary
Edited by leaders in the field, with contributions by a panel of experts, this book covers unconventional mathematics methods for image processing in remote sensing. 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 modelling, 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.
