MambaFedCD: Spatial–Spectral–Temporal Collaborative Mamba-Based Active Federated Hyperspectral Change Detection
Jiahui Qu, Jingyu Zhao, Wenqian Dong, Lijian Zhang, Yunsong Li
Abstract
Hyperspectral image (HSI) change detection is a technique that can identify the changes occurring between the bitemporal HSIs covering the same geographic area. The field of change detection has witnessed the proposal and successful implementation of numerous methods. However, a majority of these approaches adhere to the centralized learning paradigm, which requires data transmission to a central server for training. The sensitivity of remote sensing data generally prohibit their sharing across different clients. Furthermore, manual labeling is a costly effort in practically. In this paper, we propose a spatial-spectral-temporal collaborative Mamba-based active federated hyperspectral change detection (MambaFedCD) framework, which utilizes the limited labeled samples from multiple clients to achieve change detection while ensuring the data privacy of each client. Specifically, there are three key characteristics: 1) a spatial-spectral-temporal collaborative Mamba-based change detection ( ${{\text {S}}^{2}}{\text {TMamba}}$ ) model is proposed to efficiently synergize the temporal and global spatial-spectral information of the bitemporal HSIs for change detection; 2) a difference feature diversity correction-based model aggregation (DFDCMA) strategy is devised to incorporate the diversity of difference features for rational allocation of weight factors among clients and to facilitate effective aggregation of the global model; 3) we propose a multi-decision federated active learning (MDFAL) strategy that selects both error-prone and valuable samples for model training to alleviate the burden of sample labeling. Comprehensive experiments conducted on commonly utilized datasets demonstrate that the proposed method outperforms other state-of-the-art methods. The code is available at https://github.com/Jiahuiqu/MambaFedCD.