Efficient Hyperspectral Image Classification Using Spectral-Spatial Mamba Model

Belangrijkste concepten

The proposed spectral-spatial Mamba (SS-Mamba) model can effectively utilize Mamba's computational efficiency and powerful long-range feature extraction capability to achieve competitive performance in hyperspectral image classification.

Samenvatting

The paper presents a spectral-spatial Mamba (SS-Mamba) model for efficient hyperspectral image (HSI) classification. The key highlights are:

The SS-Mamba model consists of a spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks.

The token generation module converts the HSI cube into spatial and spectral tokens as sequences.
The spectral-spatial Mamba blocks employ a double-branch structure with spatial Mamba feature extraction, spectral Mamba feature extraction, and a spectral-spatial feature enhancement module.

The feature enhancement module modulates the spatial and spectral tokens using the HSI sample's center region information, allowing the model to focus on the informative regions and conduct spectral-spatial information interaction and fusion within each block.

Experiments on three widely used HSI datasets demonstrate that the proposed SS-Mamba achieves competitive results compared to state-of-the-art methods, showcasing the effectiveness of the Mamba-based approach for HSI classification.

Ablation studies verify the advantages of the spectral-spatial learning framework and the feature enhancement module, highlighting their contributions to the overall performance.

Statistieken

The proposed SS-Mamba model achieves the following classification results on the three HSI datasets:
Indian Pines dataset:

Overall Accuracy (OA): 91.59% ± 1.85%
Average Accuracy (AA): 95.46% ± 0.90%
Kappa Coefficient (K): 90.42% ± 2.07%
Pavia University dataset:

OA: 96.40% ± 0.72%
AA: 98.43% ± 0.48%
K: 95.31% ± 0.80%
Houston dataset:

OA: 94.30% ± 0.92%
AA: 94.96% ± 0.49%
K: 93.84% ± 1.01%

Citaten

"The proposed spectral-spatial Mamba (SS-Mamba) model can effectively utilize Mamba's computational efficiency and powerful long-range feature extraction capability to achieve competitive performance in hyperspectral image classification."
"The feature enhancement module modulates the spatial and spectral tokens using the HSI sample's center region information, allowing the model to focus on the informative regions and conduct spectral-spatial information interaction and fusion within each block."

Belangrijkste Inzichten Gedestilleerd Uit

Spectral-Spatial Mamba for Hyperspectral Image Classification

by Lingbo Huang... om arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18401.pdf

Diepere vragen

How can the proposed SS-Mamba model be further extended to handle hyperspectral images with different spatial and spectral resolutions

The proposed SS-Mamba model can be extended to handle hyperspectral images with different spatial and spectral resolutions by incorporating adaptive mechanisms for feature extraction and fusion. One approach could be to introduce dynamic pooling and upsampling layers to adjust for varying spatial resolutions in the input data. Additionally, the model can be enhanced with attention mechanisms that can adaptively focus on relevant spatial and spectral features based on the resolution of the input image. By incorporating these adaptive components, the SS-Mamba model can effectively handle hyperspectral images with different spatial and spectral resolutions while maintaining its classification performance.

What are the potential limitations of the Mamba-based approach, and how can they be addressed to improve its robustness and generalization capabilities

Potential limitations of the Mamba-based approach include the complexity of the model architecture, the computational resources required for training and inference, and the potential for overfitting on smaller datasets. To address these limitations and improve the robustness and generalization capabilities of the model, several strategies can be implemented. Regularization techniques such as dropout and batch normalization can be incorporated to prevent overfitting. Additionally, data augmentation methods can be used to increase the diversity of the training data and improve the model's ability to generalize to unseen samples. Furthermore, model pruning and compression techniques can be applied to reduce the computational complexity of the Mamba model without compromising its performance.

Can the spectral-spatial feature enhancement module be generalized to other deep learning architectures for hyperspectral image analysis, and what would be the potential benefits

The spectral-spatial feature enhancement module can be generalized to other deep learning architectures for hyperspectral image analysis by integrating it as a component in the feature extraction pipeline. The module can be adapted to enhance the spatial and spectral features extracted by different architectures such as CNNs, Transformers, and recurrent neural networks. By incorporating the spectral-spatial feature enhancement module, other deep learning architectures can benefit from improved information fusion and interaction between spatial and spectral features, leading to enhanced classification performance. The potential benefits of generalizing the module include improved accuracy, better feature representation, and increased robustness in hyperspectral image analysis tasks.