Frequency-Enhanced Mamba for Remote Sensing Change Detection

Abstract

Remote sensing (RS) change detection (CD) is a critical task in monitoring surface dynamics. Recently, Mamba-based methods have shown promising performance and are quickly adopted in change detection. However, when addressing the task of CD in complex scenarios, existing methods have limitations in capturing features of minor and texture changes due to the lack of frequency information. To address these challenges, we propose a frequency-enhanced Mamba for RSCD (FEMCD). First, we design a difference-guided state-space model (DGSSM) to extract change-related features. DGSSM takes the features of bitemporal images as input and uses absolute-difference features to guide the network to focus on change regions. Second, we develop a DCT-aided Mamba decoder (DCTMD) for feature decoding and refinement. DCTMD uses the omnidirectional selective scan module (OSSM) to refine the change-related features and DCT to capture minor change details. Finally, we use a simple classifier to generate the final change map. We have conducted extensive experiments on five RSCD datasets, comparing FEMCD with 11 SOTA change detectors. The experimental results show that our proposed FEMCD method outperforms other compared methods. The code can be found at: https://github.com/JYN712/FEMCD.