中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 西安光学精密机械研究所
  3. 中国科学院西安光学精密机械研究所
  4. 光学影像学习与分析中心
Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery

文献类型：期刊论文

作者Liu, Weihua1; Feng, Xiangpeng1; Wang, Shuang1; Hu, Bingliang1; Gan, Yuquan1; Zhang, Xiaorong1; Lei, Tao2
刊名INTERNATIONAL JOURNAL OF REMOTE SENSING
出版日期2018
卷号39期号:8页码:2139-2158
ISSN号0143-1161
DOI10.1080/01431161.2017.1420931
产权排序1
英文摘要With recent advances in hyperspectral imaging sensors, subtle and concealed targets that cannot be detected by multispectral imagery can be identified. The most widely used anomaly detection method is based on the Reed-Xiaoli (RX) algorithm. This unsupervised technique is preferable to supervised methods because it requires no a priori information for target detection. However, two major problems limit the performance of the RX detector (RXD). First, the background covariance matrix cannot be properly modelled because the complex background contains anomalous pixels and the images contain noise. Second, most RX-like methods use spectral information provided by data samples but ignore the spatial information of local pixels. Based on this observation, this article extends the concept of the weighted RX to develop a new approach called an adaptive saliency-weighted RXD (ASW-RXD) approach that integrates spectral and spatial image information into an RXD to improve anomaly detection performance at the pixel level. We recast the background covariance matrix and the mean vector of the RX function by multiplying them by a joint weight that in fuses spectral and local spatial information into each pixel. To better estimate the purity of the background, pixels are randomly selected from the image to represent background statistics. Experiments on two hyperspectral images showed that the proposed random selection-based ASW RXD (RSASW-RXD) approach can detect anomalies of various sizes, ranging from a few pixels to the sub-pixel level. It also yielded good performance compared with other benchmark methods.

语种英语
WOS记录号WOS:000424236900005
源URL[http://ir.opt.ac.cn/handle/181661/30777]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China;
2.Shaanxi Univ Sci & Technol, Coll Elect & Informat Engn, Xian, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Weihua,Feng, Xiangpeng,Wang, Shuang,et al. Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2018,39(8):2139-2158.
APA Liu, Weihua.,Feng, Xiangpeng.,Wang, Shuang.,Hu, Bingliang.,Gan, Yuquan.,...&Lei, Tao.(2018).Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery.INTERNATIONAL JOURNAL OF REMOTE SENSING,39(8),2139-2158.
MLA Liu, Weihua,et al."Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery".INTERNATIONAL JOURNAL OF REMOTE SENSING 39.8(2018):2139-2158.

入库方式： OAI收割

来源：西安光学精密机械研究所

浏览0
下载0
收藏0
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。