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ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression
Qizhi Fang1,2, Zixuan Wang2, Jingang Wang2
1Liaoning General Aviation Academy, Shenyang 110136, China.
Sensors (Basel, Switzerland)
|April 28, 2025
Summary
This study introduces ARM-Net, a novel hyperspectral image compression framework. ARM-Net enhances compression efficiency and accuracy by adaptively selecting bands and reconstructing spectral details.
Area of Science:
- Remote Sensing
- Computer Vision
- Data Compression
Background:
- Current hyperspectral image compression methods face high computational complexity due to the large number of spectral bands.
- Existing techniques struggle with performance under resource-constrained environments.
Purpose of the Study:
- To develop an efficient and high-fidelity hyperspectral image compression framework.
- To address the computational challenges of existing methods while preserving spectral information.
Main Methods:
- A triple-phase hybrid framework (ARM-Net) is proposed.
- Adaptive band selection is used to reduce computational load.
- High-fidelity compression of sampled band clusters and a reconstruction network for loss compensation are employed.
Main Results:
- ARM-Net demonstrates significant improvements over state-of-the-art methods on seven hyperspectral datasets.
- Achieved 1-2 dB higher peak signal-to-noise ratio (PSNR) and multiscale structural similarity index measure (MS-SSIM).
- Reduced the average spectral angle mapper (SAM) by approximately 0.1.
Conclusions:
- The proposed ARM-Net framework effectively balances compression efficiency and reconstruction quality for hyperspectral images.
- ARM-Net offers a viable solution for hyperspectral image compression in resource-limited scenarios.
