Image Processing for Remote Sensing
Crc Press Inc (Verlag)
978-1-4200-6664-7 (ISBN)
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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.
University of Massachusetts, North Dartmouth, USA
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-Ramírez and A.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, 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
| Erscheint lt. Verlag | 1.12.2007 |
|---|---|
| Zusatzinfo | 70 Halftones, black and white; 75 Tables, black and white; 27 Illustrations, color; 162 Illustrations, black and white |
| Verlagsort | Bosa Roca |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 885 g |
| Themenwelt | Technik ► Elektrotechnik / Energietechnik |
| ISBN-10 | 1-4200-6664-1 / 1420066641 |
| ISBN-13 | 978-1-4200-6664-7 / 9781420066647 |
| Zustand | Neuware |
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