Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning
Compressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder. Reconstruction algorithms play a pivotal role i...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2022/7788657 |
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| _version_ | 1832553672302657536 |
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| author | Hua Xiao Zhongliang Wang Xueying Cui Liping Wang Hongsheng Yang Yingbiao Jia |
| author_facet | Hua Xiao Zhongliang Wang Xueying Cui Liping Wang Hongsheng Yang Yingbiao Jia |
| author_sort | Hua Xiao |
| collection | DOAJ |
| description | Compressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder. Reconstruction algorithms play a pivotal role in compressive imaging systems. Traditional model-based reconstruction approaches are computationally burdensome and achieve limited success. Deep learning-based approaches, while improving in reconstruction accuracy and speed, depend heavily on data, which is a major challenge for satellite-borne hyperspectral compressed imaging. In this article, we combine the respective advantages of model-based and learning-based approaches in a distributed compressed hyperspectral sensing framework, employing linear mixed model assumptions and spectral library learning to simultaneously improve the reconstruction speed and accuracy without using a large amount of additional hyperspectral data. First, the relationship between the CS band and the key band is learned from the spectral library to ensure that the key band endmembers can be accurately predicted. Then, the joint horizontal and vertical difference operators are proposed to enhance the estimation of the initial values of abundance. Finally, the CS band endmembers and residuals are updated in the reconstruction module to deal with the endmember and abundance mismatch. Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods. |
| format | Article |
| id | doaj-art-347d5449ea4f4fe3b859669265d7b7d6 |
| institution | Kabale University |
| issn | 2314-4939 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-347d5449ea4f4fe3b859669265d7b7d62025-02-03T05:53:28ZengWileyJournal of Spectroscopy2314-49392022-01-01202210.1155/2022/7788657Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and LearningHua Xiao0Zhongliang Wang1Xueying Cui2Liping Wang3Hongsheng Yang4Yingbiao Jia5Department of Mathematics and ComputerDepartment of Electric EngineeringDepartment of Electric EngineeringEngineering Technology Research Center of Optoelectronic Technology ApplianceDepartment of Electric EngineeringSchool of Information EngineeringCompressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder. Reconstruction algorithms play a pivotal role in compressive imaging systems. Traditional model-based reconstruction approaches are computationally burdensome and achieve limited success. Deep learning-based approaches, while improving in reconstruction accuracy and speed, depend heavily on data, which is a major challenge for satellite-borne hyperspectral compressed imaging. In this article, we combine the respective advantages of model-based and learning-based approaches in a distributed compressed hyperspectral sensing framework, employing linear mixed model assumptions and spectral library learning to simultaneously improve the reconstruction speed and accuracy without using a large amount of additional hyperspectral data. First, the relationship between the CS band and the key band is learned from the spectral library to ensure that the key band endmembers can be accurately predicted. Then, the joint horizontal and vertical difference operators are proposed to enhance the estimation of the initial values of abundance. Finally, the CS band endmembers and residuals are updated in the reconstruction module to deal with the endmember and abundance mismatch. Extensive experimental results on five real hyperspectral datasets demonstrate that the proposed spectral library learning, abundance initialization, and reconstruction strategy can effectively improve the compressed sensing reconstruction accuracy, outperforming the existing state-of-the-art methods.http://dx.doi.org/10.1155/2022/7788657 |
| spellingShingle | Hua Xiao Zhongliang Wang Xueying Cui Liping Wang Hongsheng Yang Yingbiao Jia Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning Journal of Spectroscopy |
| title | Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning |
| title_full | Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning |
| title_fullStr | Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning |
| title_full_unstemmed | Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning |
| title_short | Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning |
| title_sort | distributed compressed hyperspectral sensing imaging incorporated spectral unmixing and learning |
| url | http://dx.doi.org/10.1155/2022/7788657 |
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