Synthetic aperture radar image change detection based on

frequency-domain analysis and random multigraphs

Journal of Applied Remote Sensing, 2018, January


Feng Gao, Xiao Wang, Junyu Dong, Shengke Wang

Last modified: 9, January, 2017

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 frequencydomain 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.

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Links to most related works

  1. S. Martinis, A. Twele, and S. Voigt, ※Unsupervised extraction of flood induced backscatter changes in SAR data using Markov image modeling on irregular graphs,§ IEEE Trans. Geosci. Remote Sens. 49(1), 251每263 (2011).
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