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Multiscale wavelet-based spatial–spectral compression network for hyperspectral image
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AI
DOI：10.1016/j.engappai.2025.113241.png)
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Abstract
En 中文
Hyperspectral images (HSIs) possess high-dimensional tensor structures that present significant reconstruction challenges under ultra-low compression ratios (CR) in artificial intelligence-driven remote sensing. Conventional compression methods are unable to effectively capture inherent spatial–spectral coherence and often neglect multiscale spectral absorption-reflection dependencies, which are critical for maintaining spectral fidelity. To overcome these shortcomings, we propose a Multiscale Wavelet-based Spatial-Spectral Compression Network (MWC-Net) for HSI reconstruction. Methodologically, MWC-Net integrates a three-dimensional (3D) spatial–spectral attention encoder, which via tri-branch attention to extract complete spatial–spectral coherence. Additionally, we develop a multiscale wavelet spatial–spectral decoder that restores scale-sensitive spectral features through multiscale super-resolution and enhances spatial–spectral resolution using wavelet decomposition. Compared to the state-of-the-art method “Hyperspectral Image Compression Sensing Network With Convolutional Neural Networks (CNN)–Transformer Mixture Architectures”, MWC-Net achieves an average decrease from 1.770 to 1.549 in the spectral angle mapper (SAM) metric. Additionally, the average peak signal-to-noise ratio (PSNR) increases from 39.81 to 40.79, while the average root mean square error (RMSE) decreases from 55.15 to 49.97, under approximately 1% CR. This enhancement highlights the superior ability of MWC-Net to balance compression efficiency and spectral fidelity in HSI reconstruction. The code can be available on https://github.com/YuHang-max/MWCNet .
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