WSMsFNet: Joint the Whole Supervision and Multiscale Fusion Network for Remote Sensing Image Change Detection

Abstract

Remote sensing image change detection aims to extract high-level semantic feature to identify the changed areas (CAs) between dual-temporal images (DTIs). However, the diversity in the CA shape and size poses certain challenge to the change detection (CD) task. Besides, different illumination conditions in the same scene of the DTI further increase the CD difficulty. In response to these above issues, this article proposes a multiscale feature fusion CD network-WSMsFNet, which fully utilizes the local and global information of multiscale features to achieve comprehensive representation of the change scene. In addition, the network improves the feature extraction ability of each module through the whole process supervision loss function. First, the network hierarchically extracts different scale information of the two temporal RS. Then, special information enhancement and fusion modules are constructed for various feature layers (i.e., the same level, adjacent level, and global features), aiming to enhance the local feature representation ability and contextual information relevance of the deep network. Finally, the whole-process loss function is set to supervise the intermediate layer learning, which can effectively enhance the feature representation ability and guide feature extraction direction of each module. Experiments have shown that the WSMsFNet has achieved significant results in both qualitative and quantitative indicators.

Keywords

• Change detection
• convolutional neural network (CNN)
• multiscale feature (MSF) fusion
• the whole process supervision
• transformer