FUSING MULTIPLE UNTRAINED NETWORKS FOR HYPERSPECTRAL CHANGE DETECTION

Change detection in hyperspectral images is challenging due to the presence of a large number of spectral bands. Due to the differences in band composition, a deep model trained on one hyperspectral sensor cannot be reused on another hyperspectral sensor. This challenge can be tackled by using untrained models as feature extractor for change detection in hyperspectral images. However, results produced by such a strategy may show variance if the untrained model is slightly perturbed. Different change detection maps are produced from different versions of the untrained model. We propose a decision fusion based strategy that can combine such different results and produce a final change detection map that retains the change information from all change maps. This approach improves the change detection performance and also improves reliability of the result. Experimental results on two publicly available hyperspectral datasets show the effectiveness of the proposed approach.