Unsupervised detection of flood changes with SAR images combining nonsubsampled Contourlet domain fusion and parametric kernel graph cuts

Li Qingsong; Qin Xizhong; Jia Zhenghong; Yang Jie; Hu Raphael

doi:10.11834/jig.20140619

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Unsupervised detection of flood changes with SAR images combining nonsubsampled Contourlet domain fusion and parametric kernel graph cuts
Vol. 19, Issue 6, Pages: 971-978(2014)
Published Online：06 June 2014，

Published：2014
DOI： 10.11834/jig.20140619
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DOI：

Li Qingsong, Qin Xizhong, Jia Zhenghong, Yang Jie, Hu Raphael. Unsupervised detection of flood changes with SAR images combining nonsubsampled Contourlet domain fusion and parametric kernel graph cuts[J]. Journal of Image and Graphics, 2014, 19(6): 971-978. DOI： 10.11834/jig.20140619.

摘要

基于非下采样Contourlet变换（NSCT）融合策略可以有效地抑制背景信息增强变化区域的信息。但是融合后图像具有复杂的统计特征，传统的基于统计特征的变化检测难以实现。基于参数化内核图割的遥感图像分割不受统计特征的限制。为此提出了一种基于NSCT融合和参数化内核图割的SAR图像无监督水灾变化检测新算法。将均值比差异图像和对数比差异图像采用基于NSCT的融合算法进行融合，将融合后的差异图像采用参数化内核图割算法进行前景/背景的分割，得到最终的变化检测结果。融合后的差异图像利用前两种差异图像的互补信息提高了变化检测精度。算法不受统计模型限制，不需要先验知识，适用性强。实验结果表明，本文算法的检测精度优于传统的变化检测方法。

Abstract

Fusion strategies based on a nonsubsampled contourlet transform (NSCT) are able to restrain the background information and enhance the information of changed regions in the fused difference image effectively. However the fused difference image has complex statistical characteristics

the conventional change detection based on statistical characteristics is difficult to achieve. An unsupervised detection for flood change algorithm in SAR image based on NSCT fusion and parametric kernel graph cuts is proposed in this paper. The difference images acquired by mean-ratio operation and log-ratio operation are integrated by the NSCT fusion algorithm

the fused difference image is divided into foreground and background to obtain the final change detection results by the parametric kernel graph cuts algorithm. Change detection accuracy of the fused difference image is improved by taking advantage of complementary information from mean-ratio and log-ratio images. The approach with strong applicability is distribution free

and does not need any prior knowledge. Experimental results show that the detection accuracy of the proposed algorithm is superior to the traditional change detection algorithm.