CSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification

• URL: http://arxiv.org/abs/2509.00677v1
• Date: Sun, 31 Aug 2025 03:08:34 GMT
• Title: CSFMamba: Cross State Fusion Mamba Operator for Multimodal Remote Sensing Image Classification
• Authors: Qingyu Wang, Xue Jiang, Guozheng Xu,
• Abstract summary: We propose Cross State Fusion Mamba (Camba) Network.<n>Specifically, we first design the preprocessing module of remote sensing image information for the needs of Mamba structure.<n> Secondly, a cross-state module based on Mamba operator is creatively designed to fully fuse the feature of the two modalities.
• Score: 12.959829835589453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal fusion has made great progress in the field of remote sensing image classification due to its ability to exploit the complementary spatial-spectral information. Deep learning methods such as CNN and Transformer have been widely used in these domains. State Space Models recently highlighted that prior methods suffer from quadratic computational complexity. As a result, modeling longer-range dependencies of spatial-spectral features imposes an overwhelming burden on the network. Mamba solves this problem by incorporating time-varying parameters into ordinary SSM and performing hardware optimization, but it cannot perform feature fusion directly. In order to make full use of Mamba's low computational burden and explore the potential of internal structure in multimodal feature fusion, we propose Cross State Fusion Mamba (CSFMamba) Network. Specifically, we first design the preprocessing module of remote sensing image information for the needs of Mamba structure, and combine it with CNN to extract multi-layer features. Secondly, a cross-state module based on Mamba operator is creatively designed to fully fuse the feature of the two modalities. The advantages of Mamba and CNN are combined by designing a more powerful backbone. We capture the fusion relationship between HSI and LiDAR modalities with stronger full-image understanding. The experimental results on two datasets of MUUFL and Houston2018 show that the proposed method outperforms the experimental results of Transformer under the premise of reducing the network training burden.

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