MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image Classification

In multi-source remote sensing image classification field, remarkable progress has been made by convolutional neural network and Transformer. However, existing methods are still limited due to the inherent local reductive bias. Recently, Mamba-based methods built upon the State Space Model have shown great potential for long-range dependency modeling with linear complexity, but it has rarely been explored for the multi-source remote sensing image classification task. To this end, we propose Multi-Scale Feature Fusion Mamba (MSFMamba) network for hyperspectral image (HSI) and LiDAR/SAR data joint classification. Specifically, MSFMamba mainly comprises three parts: Multi-Scale Spatial Mamba (MSpa-Mamba) block, Spectral Mamba (Spe-Mamba) block, and Fusion Mamba (Fus-Mamba) block. Specifically, to solve the feature redundancy in multiple canning routes, the MSpa-Mamba block incorporates the multi-scale strategy to minimize the computational redundancy and alleviate the feature redundancy of SSM. In addition, Spe-Mamba is designed for spectral feature exploration, which is essential for HSI feature modeling. Moreover, to alleviate the heterogeneous gap between HSI and LiDAR/SAR data, we design Fus-Mamba block for multi-source feature fusion. The original Mamba is extended to accommodate dual inputs, and cross-modal feature interaction is enhanced. Extensive experimental results on three multi-source remote sensing datasets demonstrate the superiority performance of the proposed MSFMamba over the state-of-the-art models. Source codes of MSFMamba will be made public available at https://github.com/summitgao/MSFMamba .

Sparse Focus Network for Multi-Source Remote Sensing Data Classification

Multi-source remote sensing data classification has emerged as a prominent research topic with the advancement of various sensors. Existing multi-source data classification methods are susceptible to irrelevant information interference during multi-source feature extraction and fusion. To solve this issue, we propose a sparse focus network for multi-source data classification. Sparse attention is employed in Transformer block for HSI and SAR/LiDAR feature extraction, thereby the most useful self-attention values are maintained for better feature aggregation. Furthermore, cross-attention is used to enhance multi-source feature interactions, and further improves the efficiency of cross-modal feature fusion. Experimental results on the Berlin and Houston2018 datasets highlight the effectiveness of SF-Net, outperforming existing state-of-the-art methods.

Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification

Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequately addressed the redundancy among different spectra, resulting in information leakage during the pretraining stage. This issue directly impairs the representation ability of the model. To tackle the problem, we propose a new strategy, named Mining Redundant Spectra (MRS). Unlike randomly masking spectral bands, MRS selectively masks them by similarity to increase the reconstruction difficulty. Specifically, a random spectral band is chosen during pretraining, and the selected and highly similar bands are masked. Experimental results demonstrate that employing the MRS strategy during the pretraining stage effectively improves the accuracy of existing MIM-based methods on the Berlin and Houston 2018 datasets.