ISNet: Towards Improving Separability for Remote Sensing Image Change Detection

Abstract

Deep learning has substantially pushed forward remote sensing image change detection through extracting discriminative hierarchical features. However, as the increasingly high resolution remote sensing images have abundant spatial details but limited spectral information, the use of conventional backbone networks would give rise to blurry boundaries between different semantics among hierarchical features. This explains why most false alarms in the final predictions distribute around change boundaries. To alleviate the problem, we pay attention to feature refinement and propose deep learning networks that deliver improved separability (ISNet). Our ISNet reaps the advantages from two strategies applied to refining bi-temporal feature hierarchies: (i) margin maximization that clarifies the gap between changed and unchanged semantics, and (ii) targeted arrangement of attention mechanisms that directs the use of channel attention and spatial attention for highlighting semantic and positional information, respectively. Specifically, we insert channel attention modules into share-weighted backbone networks to facilitate semantic-specific feature extraction. The semantic boundaries in the extracted bi-temporal hierarchical features are then clarified by margin maximization modules, followed by spatial attention modules to enhance positional change responses. A top-down fusion pathway makes the final refined features cover multi-scale representations and have strong separability for remote sensing image change detection. Extensive experimental evaluations demonstrate that our ISNet achieves state-of-the-art performance on the LEVIR-CD, SYSU-CD, and Season-Varying datasets, in terms of Overall Accuracy (OA), Intersection-of-Union (IoU), and F1 score. Code is available at https://github.com/xingronaldo/ISNet.