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DS-Mamba: Depthwise separable mamba for hyperspectral image classification.

Lin Wei1,2, Huihan Yang2, Yuping Yin3

  • 1Basic Teaching Department, Liaoning Technical University, Huludao, Liaoning, China.

Plos One
|March 12, 2026
PubMed
Summary
This summary is machine-generated.

DS-Mamba offers efficient hyperspectral image (HSI) classification by combining depthwise separable convolutions with Mamba architectures. This novel approach improves accuracy and reduces computational costs compared to traditional Transformers.

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Transformers face computational challenges (quadratic complexity) in hyperspectral image (HSI) classification, leading to errors and memory issues.
  • Mamba architectures offer linear computational efficiency for long-range modeling but struggle with spatial-spectral feature extraction in HSI data.

Purpose of the Study:

  • To introduce DS-Mamba, a novel depthwise separable Mamba model for enhanced HSI classification.
  • To address the limitations of basic Mamba in extracting spatial and spectral features for HSI data.

Main Methods:

  • Designed depth spatial Mamba (DSpaM) and depth spectral Mamba (DSpeM) blocks using depthwise separable convolution and Mamba.
  • Incorporated a feature enhancement module for improved spatial-spectral feature extraction and fusion.
  • Utilized Efficient Channel Attention (ECA) in the classification module for feature refinement.

Main Results:

  • DS-Mamba achieved high overall accuracies: 96.54% (Pavia University), 91.52% (Hanchuan), and 94.89% (Houston).
  • Outperformed several advanced transformer-based methods in classification performance.
  • Demonstrated significantly lower computational cost with only 137.74K parameters and 12.52G FLOPs on the Pavia University dataset.

Conclusions:

  • DS-Mamba effectively extracts spatial-spectral features for HSI classification with high accuracy.
  • The proposed model offers a computationally efficient alternative to Transformer-based methods for HSI analysis.
  • DS-Mamba shows strong potential for practical applications in hyperspectral imaging.