Hyperspectral deep convolution anomaly detection based on weight adjustment strategy | |
Chong, Dan2,3; Hu, Bingliang2; Gao, Xiaohui2; Gao, Hao4; Xia, Pu2; Wu, Yinhua1 | |
刊名 | Applied Optics |
2020-11-01 | |
卷号 | 59期号:31页码:9633-9642 |
ISSN号 | 1559128X;21553165 |
DOI | 10.1364/AO.400563 |
产权排序 | 1 |
英文摘要 | Hyperspectral anomaly detection has garnered much research in recent years due to the excellent detection ability of hyperspectral remote sensing in agriculture, forestry, geological surveys, environmental monitoring, and battlefield target detection. The traditional anomaly detection method ignores the non-linearity and complexity of the hyperspectral image (HSI), while making use of the effectiveness of spatial information rarely. Besides, the anomalous pixels and the background are mixed, which causes a higher false alarm rate in the detection result. In this paper, a hyperspectral deep net-based anomaly detector using weight adjustment strategy (WAHyperDNet) is proposed to circumvent the above issues. We leverage three-dimensional convolution instead of the two-dimensional convolution to get a better way of handling high-dimensional data. In this study, the determinative spectrum–spatial features are extracted across the correlation between HSI pixels. Moreover, feature weights in the method are automatically generated based on absolute distance and the spectral similarity angle to describe the differences between the background pixels and the pixels to be tested. Experimental results on five public datasets show that the proposed approach outperforms the state-of-the-art baselines in both effectiveness and efficiency. © 2020 Optical Society of America |
语种 | 英语 |
出版者 | OSA - The Optical Society |
WOS记录号 | WOS:000583718000001 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93821] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Gao, Xiaohui |
作者单位 | 1.Xi’an Technological University, School of Optoelectronics Engineering, No. 2 Xuefuzhonglu Road, Xi’an; 710021, China 2.Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, No. 17, Xinxi Road, Xi’an; 710119, China; 3.Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing; 100049, China; 4.Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, No. 6, KeXueYuan South Road, Haidian District, Beijing; 100190, China; |
推荐引用方式 GB/T 7714 | Chong, Dan,Hu, Bingliang,Gao, Xiaohui,et al. Hyperspectral deep convolution anomaly detection based on weight adjustment strategy[J]. Applied Optics,2020,59(31):9633-9642. |
APA | Chong, Dan,Hu, Bingliang,Gao, Xiaohui,Gao, Hao,Xia, Pu,&Wu, Yinhua.(2020).Hyperspectral deep convolution anomaly detection based on weight adjustment strategy.Applied Optics,59(31),9633-9642. |
MLA | Chong, Dan,et al."Hyperspectral deep convolution anomaly detection based on weight adjustment strategy".Applied Optics 59.31(2020):9633-9642. |
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