RaSRNet: An End-to-End Relation-Aware Semantic Reasoning Network for Change Detection in Optical Remote Sensing Images

Abstract

Optical Remote sensing images (RSIs) are used in surface observation, and one of the most interesting research topics is change detection (CD). The internal problem of RSIs, including multi-scales changed objects and cluttered background, still deserve attention. Existing methods make great efforts to solve this problem but inevitably miss detection, which affects the model performance. To address this dilemma, this article proposes a Relation-aware Semantic Reasoning Network (RaSRNet) in an end-to-end manner to pop-out change objects in RSIs, where the key point is to perceive contextual semantic information. The relation-aware module in RaSRNet combats the lack of contextual information caused by the limited receptive field of the general convolutional layer, which facilitates all-around changed object detection. The multi-level semantic reasoning encoder-decoder backbone in RaSRNet extracts and reconstructs pixel semantic information, alleviates the interference of background noise and improves the integrity recognition of changed objects. In addition, the decoder backend undertakes two semantic segmentation branches, and introduces a semantic reasoning loss between the two branches to infer pixel semantic categories, which provides more accurate semantic features for the CD. Extensive experiments are conducted on the three public RSIs CD datasets, and the results demonstrate that the proposed RaSR-Net can accurately locate changed objects, which consistently outperforms the state-of-the-art CD competitors.