Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs

Feng Gao; Xiao Wang; Junyu Dong; Shengke Wang

doi:10.1117/1.JRS.12.016010

11 January 2018 Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs

Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016010 (January 2018). https://doi.org/10.1117/1.JRS.12.016010

Abstract

With the development of earth observation programs, many multitemporal synthetic aperture radar (SAR) images over the same geographical area are available. It is demanding to develop automatic change detection techniques to take advantage of these images. Most existing techniques directly analyze the difference image (DI), and therefore, they are easily affected by the speckle noise. We proposed an SAR image change detection method based on frequency-domain analysis and random multigraphs. The proposed method follows a coarse-to-fine procedure: in the coarse changed regions localization stage, frequency-domain analysis is utilized to select distinctive and salient regions from the DI. Therefore, nonsalient regions are neglected, and noisy unchanged regions incurred by the speckle noise are suppressed. In the fine changed regions classification stage, random multigraphs are employed as the classification model. By selecting a subset of neighborhood features to create graphs, the proposed method can efficiently exploit the nonlinear relations between multitemporal SAR images. The experimental results on two real SAR datasets and one simulated dataset have demonstrated the effectiveness of the proposed method.

Citation Download Citation

Feng Gao, Xiao Wang, Junyu Dong, and Shengke Wang "Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs," Journal of Applied Remote Sensing 12(1), 016010 (11 January 2018). https://doi.org/10.1117/1.JRS.12.016010

Received: 23 May 2017; Accepted: 12 December 2017; Published: 11 January 2018

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