Non-gaussian clustering of SAR images for glacier change detection

Abstract

Our aim is to use unsupervised, non-Gaussian clustering of Arctic glaciers for post-classification change detection. Firstly, we demonstrate the consistency of non-Gaussian clustering algorithms for Envisat ASAR images by characterizing the expected random error level for different SAR acquisition conditions (such as incidence angle). This allows us to determine whether an observed variation is statistically significant and therefore can be used for post-classification change detection of Arctic glaciers. Real significant change was not detected with mixed configurations during the time period of this study.