ERMF: Edge refinement multi-feature for change detection in bitemporal remote sensing images

The change detection task plays an irreplaceable role in the remote sensing field. However, most methods ignore the edge distinctive information. Such information is not only significant in some change detection tasks such as channel and river changes, but also important for refining the accuracy of change detection. Therefore, an edge refinement multi-feature (ERMF) extraction method, employing a siamese network to extract the primary discriminative features at five scales of bitemporal remote sensing images, is proposed in this paper. On the one hand, an edge refinement module is designed to obtain the edge change map as well as the final accurate region change map. On the other hand, a multi-level feature extraction module is introduced to acquire a coarse change map consisting of low-level location information and high-level semantic information at five different scales. Besides, it is worth emphasizing that we present a hybrid loss to evaluate the ERMF model. Experiments demonstrate that the ERMF model outperforms seven state-of-the-art methods in both qualitative and quantitative evaluations.