Hyper Spectral Camera ANalyzer (HyperSCAN)
HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/842 |
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| author | Wen-Qian Chang Hsun-Ya Hou Pei-Yuan Li Michael W. Shen Cheng-Ling Kuo Tang-Huang Lin Loren C. Chang Chi-Kuang Chao Jann-Yenq Liu |
| author_facet | Wen-Qian Chang Hsun-Ya Hou Pei-Yuan Li Michael W. Shen Cheng-Ling Kuo Tang-Huang Lin Loren C. Chang Chi-Kuang Chao Jann-Yenq Liu |
| author_sort | Wen-Qian Chang |
| collection | DOAJ |
| description | HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data. |
| format | Article |
| id | doaj-art-3f3aaba150654ce8a9033d5dbaf5aaae |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-3f3aaba150654ce8a9033d5dbaf5aaae2025-08-20T02:59:15ZengMDPI AGRemote Sensing2072-42922025-02-0117584210.3390/rs17050842Hyper Spectral Camera ANalyzer (HyperSCAN)Wen-Qian Chang0Hsun-Ya Hou1Pei-Yuan Li2Michael W. Shen3Cheng-Ling Kuo4Tang-Huang Lin5Loren C. Chang6Chi-Kuang Chao7Jann-Yenq Liu8Center for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanCenter for Astronautical Physics and Engineering, National Central University, Taoyuan City 320317, TaiwanHyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data.https://www.mdpi.com/2072-4292/17/5/842Earth environmental monitoringhyperspectral imagemultispectral imagedeep learning |
| spellingShingle | Wen-Qian Chang Hsun-Ya Hou Pei-Yuan Li Michael W. Shen Cheng-Ling Kuo Tang-Huang Lin Loren C. Chang Chi-Kuang Chao Jann-Yenq Liu Hyper Spectral Camera ANalyzer (HyperSCAN) Remote Sensing Earth environmental monitoring hyperspectral image multispectral image deep learning |
| title | Hyper Spectral Camera ANalyzer (HyperSCAN) |
| title_full | Hyper Spectral Camera ANalyzer (HyperSCAN) |
| title_fullStr | Hyper Spectral Camera ANalyzer (HyperSCAN) |
| title_full_unstemmed | Hyper Spectral Camera ANalyzer (HyperSCAN) |
| title_short | Hyper Spectral Camera ANalyzer (HyperSCAN) |
| title_sort | hyper spectral camera analyzer hyperscan |
| topic | Earth environmental monitoring hyperspectral image multispectral image deep learning |
| url | https://www.mdpi.com/2072-4292/17/5/842 |
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