A High-Order Statistical Tensor Based Algorithm for Anomaly Detection in Hyperspectral Imagery

Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Mor...

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Veröffentlicht in:Scientific reports Jg. 4; H. 1; S. 6869
Hauptverfasser: Geng, Xiurui, Sun, Kang, Ji, Luyan, Zhao, Yongchao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 04.11.2014
Nature Publishing Group
Schlagworte:
639/705/1041
639/705/258
Algorithms
Denial of service attacks
Humanities and Social Sciences
multidisciplinary
Science
Statistical analysis
Statistics
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Zusammenfassung:Recently, high-order statistics have received more and more interest in the field of hyperspectral anomaly detection. However, most of the existing high-order statistics based anomaly detection methods require stepwise iterations since they are the direct applications of blind source separation. Moreover, these methods usually produce multiple detection maps rather than a single anomaly distribution image. In this study, we exploit the concept of coskewness tensor and propose a new anomaly detection method, which is called COSD (coskewness detector). COSD does not need iteration and can produce single detection map. The experiments based on both simulated and real hyperspectral data sets verify the effectiveness of our algorithm.
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ISSN:2045-2322
2045-2322
DOI:10.1038/srep06869