SPATIO-TEMPORAL FEATURES PROCESSING NETWORK FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

Change detection is a significant remote sensing challenge, which can capture changes in land use and land cover. Recently, deep learning achieves great performance in change detection, but most of the existing methods based on deep learning only process local spatial relationships and single directional temporal relationships, which severely affect the accuracy of change detection. In this paper, we present a novel end-to-end spatio-temporal processing network (STPNet) for precise change detection in remote sensing images. In our network, we design a spatial processing module which can learn long-range relationship and rich features and a temporal processing module capturing bidirectional rich contextual information, respectively. Also, we combine the two modules into a building block named spatio-temporal processing module (STPM) which can be easily incorporated into the existing siamese architectures. Experiments with the WHU building change detection dataset demonstrate that STPNet can obtain better performance than state-of-the-art methods.