DS-Mamba: Depthwise separable mamba for hyperspectral image classification
Lin Wei, Huihan Yang, Yuping Yin, Zhiyuan Qu, Haonan Zheng
TL;DR
DS-Mamba improves hyperspectral image classification by using depthwise separable Mamba blocks to efficiently extract spatial and spectral features with lower computational cost.
Contribution
Proposes DS-Mamba, a novel depthwise separable Mamba architecture for HSI classification with improved accuracy and reduced computational complexity.
Findings
DS-Mamba achieved 96.54% accuracy on the Pavia University dataset.
The model has 137.74K parameters and 12.52G FLOPs on the Pavia University dataset.
DS-Mamba outperforms advanced transformer-based methods in classification performance.
Abstract
Transformers experience quadratic computational complexity in hyperspectral image (HSI) classification tasks, which can result in error propagation and memory usage issues. Recently, Mamba architectures built upon the State Space Models have supplanted Transformers across various domains to accomplish long-range sequence modeling capability while demonstrating the advantages of linear computational efficiency. However, employing the basic Mamba model for HSI classification has problems associated with the extraction of spatial and spectral features. Motivated by this, we propose the DS-Mamba, a novel depthwise separable Mamba for HSI classification. Specifically, to extract the spatial and spectral features more efficiently, we design a depth spatial Mamba block (DSpaM), a depth spectral Mamba block (DSpeM) and a feature enhancement module. These blocks use depthwise separable…
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Taxonomy
TopicsRemote-Sensing Image Classification · Advanced Data Compression Techniques · Advanced Neural Network Applications