Automatic change detection by evidential fusion of change indices

Abstract The detection of changes affecting continental surfaces has important applications in hydrological, meteorological, and climatic modeling. We propose a way to improve mono-index change detection by a fusion of multi-index change detection results. This fusion is performed in the framework of the Dempster-Shafer evidence theory, which is particularly suited to the representation of imprecision and ignorance at the “no change”/“change” class border. Depending on the change detection index considered, we also need to determine the class number and features. This is done using the a contrario theory approach rather than classical statistical tests. The proposed algorithm is applied to forest fire damage evaluation based on three popular change indices: normalized difference values, texture evolution, and mutual information (MI). We find that change index fusion is effective at reducing both false alarm and misdetection levels, due to the complementary nature of these indices.