Abstract
This paper presents a method for analyzing Synthetic Aperture Radar (SAR) and polarimetric SAR (PolSAR) image time series based on change detection matrices (CDM) containing information on changed and unchanged pixels. These matrices are constructed for each spatial position over the time series by implementing similarity cross tests. The proposed matrix is then exploited for adaptive temporal filtering, analysis of change dynamics and multitemporal change detection. The proposed approach is illustrated on the three following data sets: 25 ascending TerraSAR-X images and 7 descending RADARSAT 2 full polarization images over Chamonix-MontBlanc, France, where the seasonal evolution of glaciers and mountains can be observed, and a time series of 11 ascending ALOS-PALSAR dual polarization images over Merapi volcano, Indonesia during a period including the 2010 eruption.
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