Feature Refinement Decomposition and Relation Preference Enhancement for Remote Sensing Change Detection

Abstract

Remote Sensing Change Detection (RSCD) is essential for identifying alterations within geographical landscapes based on dual-temporal imagery. Current methods often enhance global modeling through the use of Transformers or integrate global and local features in a coarse-grained manner. The former tends to overlook critical details, such as change boundaries and textures, while the latter defines detailed features based solely on the receptive field, with feature fusion methods often operating as a black box. In this paper, we propose a novel feature refinement methodology guided by relation preferences, specifically designed for RSCD tasks. Our approach decomposes feature extraction into components that emphasize detailed and global relationships, employing 3D small window partitioning and information-preserving extraction techniques. By minimizing mutual information between refined features and their base counterparts, we enhance feature separability, reducing information coupling. This allows for effective change detection using a simple MLP structure. Experimental results on three datasets demonstrate that our method surpasses previous state-of-the-art models, achieving 89.01% IoU, 63.29% IoU, and 84.73% IoU on the Google, CLCD, and PRELEVIR datasets, respectively, while maintaining superior performance and computational efficiency. Code can be available: https://github.com/mxx123321/remote_sensing.