Adversarial Feature Equilibrium Network for Multimodal Change Detection in Heterogeneous Remote Sensing Images

Yan Pu, Maoguo Gong, Tongfei Liu, Mingyang Zhang, Tianqi Gao, Fenlong Jiang, Xiaobo Hu

Abstract: Change detection (CD) methods have been crucial in exploring geo-environmental science. With the advancement of remote sensing (RS) technology, multimodal images acquired from different platforms and sensors are widely used for CD tasks. As an emerging task, multimodal CD (MCD) aims to achieve more comprehensive and precise detection of land cover changes through complementary information in multimodal images. However, there are significant differences between modalities, particularly in heterogeneous images. How to deal with modal differences while effectively integrating change information remains a challenge in MCD. In this article, we propose a novel adversarial feature equilibrium network (AFENet), which establishes an additional adversarial optimization to solve the equilibrium problem between modal differences and land cover changes. Our AFENet aligns the features and reduces the modal gap through a multiscale adversarial domain adaptation (MADA) approach. Meanwhile, a divergence-aware contrastive module (DCM) is designed as a regularization term for adversarial optimization. DCM affects the sensitivity of feature extractors by constraining the mutual information between changed and unchanged pixels. In this case, AFENet can maintain the consistency of feature representation while maximizing the discriminability of change targets. The features extracted from AFENet will then be integrated by our multistream feature fusion (MFF) module and utilized to generate change maps. The effectiveness of our approach is demonstrated on two scene-level multimodal RS datasets. Compared with existing methods, our AFENet achieves state-of-the-art (SOTA) performance on both datasets and outperforms the second-best $F1$ score by 4.64% and 1.1%, respectively.