Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology
Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2021/6629661 |
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| _version_ | 1849306334117232640 |
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| author | Donge Zhao Shuyan Liu Xuefeng Yang Yayun Ma Bin Zhang Wenbo Chu |
| author_facet | Donge Zhao Shuyan Liu Xuefeng Yang Yayun Ma Bin Zhang Wenbo Chu |
| author_sort | Donge Zhao |
| collection | DOAJ |
| description | Hyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance. |
| format | Article |
| id | doaj-art-2e86ca884c8b47c6ba97aa1ecbf15af6 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-2e86ca884c8b47c6ba97aa1ecbf15af62025-08-20T03:55:07ZengWileyJournal of Spectroscopy2314-49202314-49392021-01-01202110.1155/2021/66296616629661Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging TechnologyDonge Zhao0Shuyan Liu1Xuefeng Yang2Yayun Ma3Bin Zhang4Wenbo Chu5School of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaSchool of Information and Communication Engineering, North University of China, Taiyuan 030051, ChinaHyperspectral imaging technology can obtain the spatial information and spectral information of the simulated operational background and its camouflage materials at the same time and identify and classify them according to their differences. In this paper, we collected the hyperspectral images (400–1000 nm) of the desert background, jungle background, desert camouflage netting, jungle camouflage netting, and jungle camouflage clothing through the hyperspectral imaging system, and the samples were preprocessed by denoising and black-and-white correction. Then, we analysed the region of interest (ROI) of the training samples by principal component analysis (PCA). After the pixels in the region of interest and their surrounding areas were averaged, 60% of the data was used as the training samples, and the remaining 40% was used as the test samples. According to their similarities and differences between them and referenced spectrum, the models of classification were established by combining the Naive Bayes (NB) algorithm, K-nearest neighbour (KNN) algorithm, random forest (RF) algorithm, and support vector machine (SVM) algorithm. The results show that among the four models, SVM model has the highest accuracy of classification and the recognition rate of jungle camouflage clothing is the highest. This study verifies the scientific and feasibility of hyperspectral imaging technology for camouflage identification and classification in a simulated operational environment, which has some practical significance.http://dx.doi.org/10.1155/2021/6629661 |
| spellingShingle | Donge Zhao Shuyan Liu Xuefeng Yang Yayun Ma Bin Zhang Wenbo Chu Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology Journal of Spectroscopy |
| title | Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology |
| title_full | Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology |
| title_fullStr | Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology |
| title_full_unstemmed | Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology |
| title_short | Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology |
| title_sort | research on camouflage recognition in simulated operational environment based on hyperspectral imaging technology |
| url | http://dx.doi.org/10.1155/2021/6629661 |
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