Random selection-based adaptive saliency-weighted RXD anomaly detection for hyperspectral imagery | |
Liu, Weihua1; Feng, Xiangpeng1; Wang, Shuang1; Hu, Bingliang1; Gan, Yuquan1; Zhang, Xiaorong1; Lei, Tao2 | |
作者部门 | 光谱成像技术实验室 |
2018 | |
发表期刊 | INTERNATIONAL JOURNAL OF REMOTE SENSING |
ISSN | 0143-1161 |
卷号 | 39期号:8页码:2139-2158 |
产权排序 | 1 |
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.
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DOI | 10.1080/01431161.2017.1420931 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000424236900005 |
EI入藏号 | 20180604764681 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/30777 |
专题 | 光谱成像技术研究室 |
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. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Random selection-bas(3205KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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