AgF2Net: Attention-Guided Feature Fusion Network for Multitemporal Hyperspectral Image Change Detection

Hyperspectral (HS) image change detection (CD) is an integral component of multitemporal remote-sensing (RS) Earth observation research. However, the existing HS image CD technology still has some problems, such as insufficient effective information extraction and weak correlation between shallow information and deep information, and so on. This letter proposes a new approach called attention-guided feature fusion network for multitemporal HS image CD (AgF(2)Net). This method is capable of efficiently retrieving and combining spatial-spectral (SS) features extracted from both shallow and deep layers of HS images, thereby enhancing the network's capability to capture features from multitemporal HS images. The attention-guided enhanced joint feature extraction strategy is used to obtain a better change discriminative feature representation, and the multilevel features extracted from the backbone network are combined between spatial information and spectral information. The integrated channel feature fusion module (ICFFM) not only solves the problem of insufficient feature fusion at different levels, but also strengthens effective semantic information and forms features with more discriminative ability, while also realizing the advantages of multiple features and enhancing the network's robustness and the accuracy of CD results. According to experimental results obtained from three publicly available datasets for detecting changes in HS images, the findings indicate that the proposed AgF(2)Net outperforms most advanced state-of-the-art (SOTA) methods. The source code of the AgF(2)Net will be public on https://github.com/NWH/AgF(2)Net.