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Geophysical Institute, University of Alaska Fairbanks, 903 Koyukuk Drive, P.O. Box 757320, Fairbanks, AK 99775, USA





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Academic Editors: Richard Gloaguen and Prasad S. Thenkabail

Abstract Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar SAR images remains a difficult task. This is, in large part, due to a the high level of speckle noise that is inherent to SAR data; b the complex scattering response of SAR even for rather homogeneous targets; c the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry incident angle, orbit path, etc.; and d the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales resolution levels. The developed method follows a three-step approach of i initial pre-processing; ii data enhancement-filtering; and iii wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska. View Full-Text

Keywords: change detection; SAR; decision support; image decomposition; image analysis; Bayesian inferencing change detection; SAR; decision support; image decomposition; image analysis; Bayesian inferencing





Autor: Olaniyi A. Ajadi * , Franz J. Meyer and Peter W. Webley

Fuente: http://mdpi.com/



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