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Published on: June 18, 2021
Spectral State Fusion Tree Mamba for Hyperspectral Image Classification
Summary
This study introduces Spectral State Fusion Tree Mamba (SSFTM) for hyperspectral image (HSI) classification. SSFTM improves spatial-spectral feature extraction and achieves superior accuracy over existing methods.
Area of Science:
- Remote Sensing
- Computer Vision
- Artificial Intelligence
Background:
- Hyperspectral image (HSI) data present challenges in classification due to complex spatial structures and high-dimensional spectral information.
- Traditional Mamba models face limitations in HSI classification, including restricted receptive fields, high computational costs, and suboptimal spatial-spectral relationship modeling.
- Existing methods fail to adapt scanning paths based on spectral similarity and overlook inter-channel spectral feature extraction.
Purpose of the Study:
- To propose a novel Spectral State Fusion Tree Mamba (SSFTM) architecture for enhanced HSI classification.
- To address the limitations of traditional Mamba in capturing spatial-spectral dependencies and spectral feature extraction.
- To improve the efficiency and accuracy of hyperspectral image classification.
Main Methods:
- Introduced the Tree Scan (TS) mechanism to construct adaptive minimum spanning trees in spatial and spectral domains, optimizing spatial-spectral relationships.
- Developed the Spectral State Fusion (SSF) mechanism using multi-layer one-dimensional dilated convolutions for inter-channel spectral feature extraction.
- Implemented the SSFTM architecture for hyperspectral image classification tasks.
Main Results:
- The proposed SSFTM architecture achieved superior classification accuracy on multiple benchmark datasets compared to state-of-the-art (SOTA) methods.
- SSFTM demonstrated effective joint feature extraction in both spatial and spectral domains.
- The model exhibited acceptable computational complexity, making it a practical solution for HSI classification.
Conclusions:
- The SSFTM architecture effectively overcomes the limitations of traditional Mamba for HSI classification.
- The novel TS and SSF mechanisms enable adaptive spatial-spectral relationship modeling and multi-scale spectral feature extraction.
- SSFTM offers a promising approach for accurate and efficient hyperspectral image classification.
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