干旱区生态环境知识资源中心(Arid): Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology

Arid

DOI	10.1155/2021/6629661
	Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology
	Zhao, Donge; Liu, Shuyan; Yang, Xuefeng; Ma, Yayun; Zhang, Bin; Chu, Wenbo
通讯作者	Zhao, DG (corresponding author), North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Peoples R China. ; Zhao, DG (corresponding author), North Univ China, Shanxi Prov Res Ctr Optoelect Informat & Instrume, Taiyuan 030051, Peoples R China.
来源期刊	JOURNAL OF SPECTROSCOPY
ISSN	2314-4920
EISSN	2314-4939
出版年	2021
卷号	2021
英文摘要	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.
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000644197000001
WOS类目	Biochemical Research Methods ; Spectroscopy
WOS研究方向	Biochemistry & Molecular Biology ; Spectroscopy
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351009
作者单位	[Zhao, Donge; Liu, Shuyan; Yang, Xuefeng; Ma, Yayun; Zhang, Bin; Chu, Wenbo] North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Peoples R China; [Zhao, Donge] North Univ China, Shanxi Prov Res Ctr Optoelect Informat & Instrume, Taiyuan 030051, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Donge,Liu, Shuyan,Yang, Xuefeng,et al. Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology[J],2021,2021.
APA	Zhao, Donge,Liu, Shuyan,Yang, Xuefeng,Ma, Yayun,Zhang, Bin,&Chu, Wenbo.(2021).Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology.JOURNAL OF SPECTROSCOPY,2021.
MLA	Zhao, Donge,et al."Research on Camouflage Recognition in Simulated Operational Environment Based on Hyperspectral Imaging Technology".JOURNAL OF SPECTROSCOPY 2021(2021).