MULTI-TEMPORAL IMAGES CLASSIFICATION WITH EVIDENTIAL FUSION OF MANIFOLD ALIGNMENT

Multi-temporal remote sensing images classification have attracted more and more attention in the last decade because of a wide range of applications of multi-temporal images in long-term environmental monitoring and land cover change detection and increasing multi-temporal data available. At present, most papers investigated two temporal remote-sensing images classification. In fact, there is lots of distinctive information to be unexploited between two or more temporal images which can enhance classification effect and improve ability of detecting change area. In this paper, we present an evidential fusion framework of manifold alignment to combine more than two multi-temporal remote sensing images. Embedding of multi-groups two temporal images pairs after MA can be intergraded based a layered structure of D-S theory. The proposed method was evaluated using five Landsat 8 images. Results confirmed that the proposed algorithm performed better than those with only two temporal images.