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Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach

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
Remote Sens. 2016, 8(6), 482; https://doi.org/10.3390/rs8060482
Received: 24 March 2016 / Revised: 3 May 2016 / Accepted: 2 June 2016 / Published: 8 June 2016
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
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MDPI and ACS Style

Ajadi, O.A.; Meyer, F.J.; Webley, P.W. Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach. Remote Sens. 2016, 8, 482. https://doi.org/10.3390/rs8060482

AMA Style

Ajadi OA, Meyer FJ, Webley PW. Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach. Remote Sensing. 2016; 8(6):482. https://doi.org/10.3390/rs8060482

Chicago/Turabian Style

Ajadi, Olaniyi A., Franz J. Meyer, and Peter W. Webley 2016. "Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach" Remote Sensing 8, no. 6: 482. https://doi.org/10.3390/rs8060482

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