HFIFNet: Hierarchical Feature Interaction Network With Multiscale Fusion for Change Detection

Abstract

Change detection (CD) from remote sensing images has been widely used in land management and urban planning. Benefiting from deep learning, numerous methods have achieved significant results in the CD of clearly changed targets. However, there are still significant challenges in the CD of weak targets, such as targets with small size, targets with blurred boundaries, and targets with low distinguishability from the background. Feature extraction from these targets can result in the loss of critical spatial features, potentially leading to decreased CD performance. Inspired by the improvement of multiscale features for CD of weak target, a hierarchical feature interaction network with multiscale fusion was proposed. First, a hierarchical feature interactive fusion module is proposed, which achieves optimized multichannel feature interaction and enhances the distinguishability between weak targets and background. Moreover, the module also achieves cross scale feature fusion, which compensates for the loss of spatial feature of changed targets at a single scale during feature extraction. Second, VMamba Block is utilized to obtain global features, and a spatial feature localization module was proposed to enhance the saliency of spatial features such as edges and textures. The distinguishability between weak targets and irrelevant spatial features is further enhanced. Our method has been experimentally evaluated on three public datasets, and outperformed state-of-the-art approaches by 1.06%, 1.41%, and 2.63% in F1 score on the LEVIR-CD, S2Looking, and NALand datasets, respectively. These results affirm the effectiveness of our method for weak targets in CD tasks.