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Sensors 2018, 18(2), 559; doi:10.3390/s18020559

An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Author to whom correspondence should be addressed.
Received: 31 December 2017 / Revised: 30 January 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
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The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed to detect change based on time-series data from different sensors. Firstly, the overall difference image of the time-series PolSAR is calculated by omnibus test statistics, and difference images between any two images in different times are acquired by Rj test statistics. Secondly, the difference images are segmented with a Generalized Statistical Region Merging (GSRM) algorithm which can suppress the effect of speckle noise. Generalized Gaussian Mixture Model (GGMM) is then used to obtain the time-series change detection maps in the final step of the proposed method. To verify the effectiveness of the proposed method, we carried out the experiment of change detection using time-series PolSAR images acquired by Radarsat-2 and Gaofen-3 over the city of Wuhan, in China. Results show that the proposed method can not only detect the time-series change from different sensors, but it can also better suppress the influence of speckle noise and improve the overall accuracy and Kappa coefficient. View Full-Text
Keywords: time-series; unsupervised change detection; PolSAR; omnibus test statistic; GSRM; GGMM time-series; unsupervised change detection; PolSAR; omnibus test statistic; GSRM; GGMM

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, W.; Yang, J.; Zhao, J.; Shi, H.; Yang, L. An Unsupervised Change Detection Method Using Time-Series of PolSAR Images from Radarsat-2 and GaoFen-3. Sensors 2018, 18, 559.

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