PMCMA: Pattern Mining in SAR Time Series by Change Matrix Analysis

Abstract

This paper presents a novel change detection scheme for synthetic aperture radar (SAR) time series, named Pattern Mining by Change Matrix Analysis (PMCMA). This scheme involves three steps: 1) change detection in SAR time series via the statistic of change matrix; 2) change matrix clustering by the simultaneous clustering and model selection (SCAMS) algorithm; 3) change pattern classification using the clustering results of change matrices. The procedure is executed with an automatic clustering algorithm and does not require the default number of clusters. The proposed approach is tested on two SAR time series of 12 TerraSAR-X images acquired from September, 2013 to October, 2014 over the Shanghai, China. Experimental results show the effectiveness of the proposed PMCMA scheme.