Progressive Refinement Network for Remote Sensing Image Change Detection

Change detection (CD) in high-resolution remote sensing images (RSIs) aims at locating and understanding surface change areas. Despite some models have been proposed to solve the intrinsic problems of CD in RSIs (e.g., scale variation and internal nonconsistency), they resulted in less than ideal outcomes in specific scenes, such as objects with the same semantic concept but different spectrums and irrelevant change objects in the background. To this end, this article proposes a progressive refinement network (PRNet) to explore changes in more complex scenes in a continual calibration way. First, we excavate focused interactive deep semantic information with a proposed semantic refinement (SR) module based on the Vision Transformer and graph representation, which understands more useful semantic relations in ground objects. Second, we design a self-refinement (Self-R) module based on the supervised filtering framework to refine the shallow decoded features progressively. In addition, to ensure the structural information of the ground objects to the maximum extent, we propose a local detail enhancement (LDE) module based on multiscale convolutional architectures at the low-level encoding stage. Comprehensive experimental results on the two-instance RSI CD datasets and two public CD datasets demonstrate that the proposed PRNet achieves competitive performance with fewer parameters (3.44 M).