Research Papers

Probabilistic anomaly detector for remotely sensed hyperspectral data

[+] Author Affiliations
Lianru Gao

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Qiandong Guo

University of South Florida, School of Geosciences, 4202 East Fowler Avenue, Tampa, Florida 33620, United States

Antonio Plaza

University of Extremadura, Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Avda. de la Universidad S/N, Cáceres 10071, Spain

Jun Li

Sun Yat-sen University, School of Geography and Planning, No. 135 Xingang Xi Road, Guangzhou 510275, China

Bing Zhang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

J. Appl. Remote Sens. 8(1), 083538 (Nov 03, 2014). doi:10.1117/1.JRS.8.083538
History: Received March 24, 2014; Revised September 12, 2014; Accepted September 25, 2014
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Abstract.  Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.

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Citation

Lianru Gao ; Qiandong Guo ; Antonio Plaza ; Jun Li and Bing Zhang
"Probabilistic anomaly detector for remotely sensed hyperspectral data", J. Appl. Remote Sens. 8(1), 083538 (Nov 03, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083538


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