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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
Department光谱成像技术实验室
2018
Source PublicationINTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN0143-1161
Volume39Issue:8Pages:2139-2158
Contribution Rank1
Abstract

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.

 

DOI10.1080/01431161.2017.1420931
Indexed BySCI ; EI
Language英语
WOS IDWOS:000424236900005
EI Accession Number20180604764681
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30777
Collection光谱成像技术实验室
Affiliation1.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
Recommended Citation
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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