Synthetic aperture radar image change detection based
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
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
[ MATLAB code ]
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