ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression
Most current hyperspectral image compression methods rely on well-designed modules to capture image structural information and long-range dependencies. However, these modules tend to increase computational complexity exponentially with the number of bands, which limits their performance under constr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1843 |
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| author | Qizhi Fang Zixuan Wang Jingang Wang Lili Zhang |
| author_facet | Qizhi Fang Zixuan Wang Jingang Wang Lili Zhang |
| author_sort | Qizhi Fang |
| collection | DOAJ |
| description | Most current hyperspectral image compression methods rely on well-designed modules to capture image structural information and long-range dependencies. However, these modules tend to increase computational complexity exponentially with the number of bands, which limits their performance under constrained resources. To address these challenges, this paper proposes a novel triple-phase hybrid framework for hyperspectral image compression. The first stage utilizes an adaptive band selection technique to sample the raw hyperspectral image, which mitigates the computational burden. The second stage concentrates on high-fidelity compression, efficiently encoding both spatial and spectral information within the sampled band clusters. In the final stage, a reconstruction network compensates for sampling-induced losses to precisely restore the original spectral details. The proposed framework, known as ARM-Net, is evaluated on seven mixed hyperspectral datasets. Compared to state-of-the-art methods, ARM-Net achieves an overall improvement of approximately 1–2 dB in both the peak signal-to-noise ratio and multiscale structural similarity index measure, as well as a reduction in the average spectral angle mapper of approximately 0.1. |
| format | Article |
| id | doaj-art-6040e80a5c484f98b5e4dee9018becbe |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-6040e80a5c484f98b5e4dee9018becbe2025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256184310.3390/s25061843ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image CompressionQizhi Fang0Zixuan Wang1Jingang Wang2Lili Zhang3Liaoning General Aviation Academy, Shenyang 110136, ChinaSchool of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Electronical and Information Engineering, Shenyang Aerospace University, Shenyang 110136, ChinaMost current hyperspectral image compression methods rely on well-designed modules to capture image structural information and long-range dependencies. However, these modules tend to increase computational complexity exponentially with the number of bands, which limits their performance under constrained resources. To address these challenges, this paper proposes a novel triple-phase hybrid framework for hyperspectral image compression. The first stage utilizes an adaptive band selection technique to sample the raw hyperspectral image, which mitigates the computational burden. The second stage concentrates on high-fidelity compression, efficiently encoding both spatial and spectral information within the sampled band clusters. In the final stage, a reconstruction network compensates for sampling-induced losses to precisely restore the original spectral details. The proposed framework, known as ARM-Net, is evaluated on seven mixed hyperspectral datasets. Compared to state-of-the-art methods, ARM-Net achieves an overall improvement of approximately 1–2 dB in both the peak signal-to-noise ratio and multiscale structural similarity index measure, as well as a reduction in the average spectral angle mapper of approximately 0.1.https://www.mdpi.com/1424-8220/25/6/1843hyperspectral image compressionspectral reconstructionspatial-spectral attention mechanismrecurrent spectral attention mechanism |
| spellingShingle | Qizhi Fang Zixuan Wang Jingang Wang Lili Zhang ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression Sensors hyperspectral image compression spectral reconstruction spatial-spectral attention mechanism recurrent spectral attention mechanism |
| title | ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression |
| title_full | ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression |
| title_fullStr | ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression |
| title_full_unstemmed | ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression |
| title_short | ARM-Net: A Tri-Phase Integrated Network for Hyperspectral Image Compression |
| title_sort | arm net a tri phase integrated network for hyperspectral image compression |
| topic | hyperspectral image compression spectral reconstruction spatial-spectral attention mechanism recurrent spectral attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/6/1843 |
| work_keys_str_mv | AT qizhifang armnetatriphaseintegratednetworkforhyperspectralimagecompression AT zixuanwang armnetatriphaseintegratednetworkforhyperspectralimagecompression AT jingangwang armnetatriphaseintegratednetworkforhyperspectralimagecompression AT lilizhang armnetatriphaseintegratednetworkforhyperspectralimagecompression |