FreMamba: A Frequency-Domain Mamba Model for Hyperspectral Image Change Detection

The task of hyperspectral image change detection (HSI-CD) is to identify the subtle category changes of land surfaces by utilizing the rich spectral information of bi-temporal hyperspectral images (HSIs). Recent advanced deep learning methods have improved the performance of HSI-CD. However, HSIs have the mixed-pixel phenomenon, which affects the ability of HSI-CD models to discriminate land-cover changes. In this paper, a frequency domain Mamba (FreMamba) model is proposed to precisely capture the details of the spatial and spectral changes in bi-temporal HSIs, with consideration of the abovementioned problem. The proposed FreMamba model utilizes the capability of the Mamba model to adaptively filter the critical change information in long-range feature sequences. This is combined with frequency domain information to enhance the low-frequency global information and high-frequency spatial detail representation, based on a dual-branch network structure. A low-frequency spectral-guided attention (LSGA) module is proposed for the low-frequency Mamba branch, where it is embedded within the Mamba block via residual connections. Global low-frequency features are aggregated by the LSGA module with noise suppression, while the spectral change discriminability of ground objects is adaptively enhanced via the subsequent Mamba blocks. In the high-frequency Mamba branch, a high-frequency spatial geometric enhancement (HSGE) module is proposed that preserves edge details by spatial frequency domain decomposition to extract high-frequency geometric features. Experiments on three public HSI-CD datasets demonstrate that the proposed FreMamba model can accurately capture detailed change information and can outperform the existing state-of-the-art HSI-CD methods.