Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 965-970, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-965-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Sep 2017
A STUDY OF UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND GAUSSIAN MIXTURE MODEL USING POLSAR SAR DATA
Y. Yang1 and W. Liu2 1Middle School Affiliated to Central China Normal University , 430079 Wuhan, China
2The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Wuhan, China
Keywords: PolSAR, change detection, test statistic, GMM, flood disaster in Wuhan city Abstract. To solve the problems of existing method of change detection using fully polarimetric SAR which not takes full advantage of polarimetric information and the result of false alarm rate of which is high, a method is proposed based on test statistic and Gaussian mixture model in this paper. In the case of the flood disaster in Wuhan city in 2016, difference image is obtained by the likelihoodratio parameter which is built using coherency matrix C3 or covariance matrix T3 of fully polarimetric SAR based on test statistic, and it becomes a reality that the change information is automatic extracted by the parameter of Gaussian mixture model (GMM) of difference image based on the expectation maximization (EM) iterative algorithm. The experimental results show that the overall accuracy of change detection results can be improved and false alarm rate can be reduced using this method by comparison with traditional constant false alarm rate (CFAR) method. Thus the validity and feasibility of the method is demonstrated.
Conference paper (PDF, 1632 KB)


Citation: Yang, Y. and Liu, W.: A STUDY OF UNSUPERVISED CHANGE DETECTION BASED ON TEST STATISTIC AND GAUSSIAN MIXTURE MODEL USING POLSAR SAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 965-970, https://doi.org/10.5194/isprs-archives-XLII-2-W7-965-2017, 2017.

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