Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection
文献类型:期刊论文
作者 | Wang, Nan2,3; Shi, Yuetian2,3; Cheng, Yinzhu2,3; Yang, Fanchao1,3; Zhang, Geng1,3; Li, Siyuan1,3; Liu, Xuebin1,3 |
刊名 | Journal of Applied Remote Sensing |
出版日期 | 2023-07-01 |
卷号 | 17期号:3 |
ISSN号 | 19313195 |
关键词 | anomaly detection hyperspectral imagery remote sensing collaborative representation |
DOI | 10.1117/1.JRS.17.034511 |
产权排序 | 1 |
英文摘要 | Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised strategy to implement HAD by dimensionality reduction (DR) and prior-based collaborative representation with adaptive global salient weight. The proposed framework includes three main steps. First, we select the most discriminating bands as the input hyperspectral images for subsequent processing in a DR manner. Then, we apply piecewise-smooth prior and local salient prior to collaborative representation to produce the initial detection map. Finally, to generate the final detection map, a global adaptive salient map is applied to the initial anomaly map to further highlight anomalies. Most importantly, the experimental results show that the proposed method outperforms alternative detectors on several datasets over different scenes. In particular, on the Gulfport dataset, the area under the curve value obtained by the proposed method is 0.9932, which is higher than the second-best method, convolutional neural network detector, by 0.0071. © 2023 Society of Photo-Optical Instrumentation Engineers (SPIE). |
语种 | 英语 |
出版者 | SPIE |
WOS记录号 | WOS:001077860300016 |
源URL | [http://ir.opt.ac.cn/handle/181661/96836] |
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 | Zhang, Geng |
作者单位 | 1.Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi'an, China 2.University of Chinese Academy of Sciences, Beijing, China; 3.Chinese Academy of Sciences, Xi'an Institute of Optics and Precision Mechanics, Key Laboratory of Spectral Imaging Technology, Xi'an, China; |
推荐引用方式 GB/T 7714 | Wang, Nan,Shi, Yuetian,Cheng, Yinzhu,et al. Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection[J]. Journal of Applied Remote Sensing,2023,17(3). |
APA | Wang, Nan.,Shi, Yuetian.,Cheng, Yinzhu.,Yang, Fanchao.,Zhang, Geng.,...&Liu, Xuebin.(2023).Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection.Journal of Applied Remote Sensing,17(3). |
MLA | Wang, Nan,et al."Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection".Journal of Applied Remote Sensing 17.3(2023). |
入库方式: OAI收割
来源:西安光学精密机械研究所
其他版本
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。