Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model
Advanced deep-learning methodologies have led to notable improvements in hyperspectral image compression. While most existing deep learning approaches primarily concentrate on reducing spatial redundancy, the challenge of addressing spectral redundancy remains unresolved. Furthermore, the implementa...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11061775/ |
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| author | Jiahui Liu Lili Zhang Jingang Wang Lele Qu |
| author_facet | Jiahui Liu Lili Zhang Jingang Wang Lele Qu |
| author_sort | Jiahui Liu |
| collection | DOAJ |
| description | Advanced deep-learning methodologies have led to notable improvements in hyperspectral image compression. While most existing deep learning approaches primarily concentrate on reducing spatial redundancy, the challenge of addressing spectral redundancy remains unresolved. Furthermore, the implementation of current models in resource-limited settings is often impeded by their high parameter counts and computational demands. To address these challenges, we propose a lightweight band-adaptive hyperspectral image compression model (LBA-HIM) aimed at enhancing compression efficiency while ensuring low computational overhead. LBA-HIM incorporates a feature decoupling mechanism that effectively separates hyperspectral images into fundamental and detailed features, thereby facilitating the reduction of spatial redundancy while preserving critical image details. In addition, a recurrent structure is integrated into the band encoding and decoding processes, enabling the utilization of prior information from previously processed bands in subsequent operations. This strategy contributes to more efficient data compression by minimizing spectral redundancy. An adaptive weighted fusion mechanism is also employed to optimize the integration of multilevel features. Evaluation across six hyperspectral datasets indicates that the LBA-HIM model significantly enhances both compression efficiency and image quality while simultaneously lowering computational costs. At a compression rate of 0.25 bits per pixel per band, LBA-HIM achieves an average peak signal-to-noise ratio of 38.25 dB, which represents an improvement of approximately 2.5 dB over the current state-of-the-art techniques. |
| format | Article |
| id | doaj-art-b98e26d7656742caa4f1ba1bd2fea070 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-b98e26d7656742caa4f1ba1bd2fea0702025-08-20T02:39:59ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118167331674910.1109/JSTARS.2025.358493111061775Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent ModelJiahui Liu0https://orcid.org/0009-0006-6421-7833Lili Zhang1https://orcid.org/0000-0002-9287-3612Jingang Wang2Lele Qu3https://orcid.org/0000-0002-2794-8892College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, ChinaCollege of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, ChinaCollege of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, ChinaCollege of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang, ChinaAdvanced deep-learning methodologies have led to notable improvements in hyperspectral image compression. While most existing deep learning approaches primarily concentrate on reducing spatial redundancy, the challenge of addressing spectral redundancy remains unresolved. Furthermore, the implementation of current models in resource-limited settings is often impeded by their high parameter counts and computational demands. To address these challenges, we propose a lightweight band-adaptive hyperspectral image compression model (LBA-HIM) aimed at enhancing compression efficiency while ensuring low computational overhead. LBA-HIM incorporates a feature decoupling mechanism that effectively separates hyperspectral images into fundamental and detailed features, thereby facilitating the reduction of spatial redundancy while preserving critical image details. In addition, a recurrent structure is integrated into the band encoding and decoding processes, enabling the utilization of prior information from previously processed bands in subsequent operations. This strategy contributes to more efficient data compression by minimizing spectral redundancy. An adaptive weighted fusion mechanism is also employed to optimize the integration of multilevel features. Evaluation across six hyperspectral datasets indicates that the LBA-HIM model significantly enhances both compression efficiency and image quality while simultaneously lowering computational costs. At a compression rate of 0.25 bits per pixel per band, LBA-HIM achieves an average peak signal-to-noise ratio of 38.25 dB, which represents an improvement of approximately 2.5 dB over the current state-of-the-art techniques.https://ieeexplore.ieee.org/document/11061775/Adaptive weighted fusionband-adaptive compressionfeature decouplinghyperspectral image compressionlightweight modelrecurrent structure |
| spellingShingle | Jiahui Liu Lili Zhang Jingang Wang Lele Qu Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adaptive weighted fusion band-adaptive compression feature decoupling hyperspectral image compression lightweight model recurrent structure |
| title | Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model |
| title_full | Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model |
| title_fullStr | Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model |
| title_full_unstemmed | Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model |
| title_short | Lightweight Band-Adaptive Hyperspectral Image Compression With Feature Decouple and Recurrent Model |
| title_sort | lightweight band adaptive hyperspectral image compression with feature decouple and recurrent model |
| topic | Adaptive weighted fusion band-adaptive compression feature decoupling hyperspectral image compression lightweight model recurrent structure |
| url | https://ieeexplore.ieee.org/document/11061775/ |
| work_keys_str_mv | AT jiahuiliu lightweightbandadaptivehyperspectralimagecompressionwithfeaturedecoupleandrecurrentmodel AT lilizhang lightweightbandadaptivehyperspectralimagecompressionwithfeaturedecoupleandrecurrentmodel AT jingangwang lightweightbandadaptivehyperspectralimagecompressionwithfeaturedecoupleandrecurrentmodel AT lelequ lightweightbandadaptivehyperspectralimagecompressionwithfeaturedecoupleandrecurrentmodel |