Journal of Applied Sciences ›› 2012, Vol. 30 ›› Issue (1): 82-88.doi: 10.3969/j.issn.0255-8297.2012.01.013
• Signal and Information Processing • Previous Articles Next Articles
CHENG Bao-zhi, ZHAO Chun-hui, WANG Yu-lei
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Abstract:
Without considering background interferences, the classical algorithm of support vector data description (SVDD) has relatively low detection probability in hyperspectral anomaly detection. To solve the problem, this paper presented a new algorithm based SVDD, which includes hyperspectral unmixing to separate target information from complicated background clutter. After spectral unmixing, the error datum includes abundant target information while effectively suppresses the background interference. The error datum is then mapped into a high-dimensional feature space with nonlinear SVDD. By exploiting nonlinear information between the spectral bands of hyperspectral imagery, anomaly targets are detected. The results show that the proposed algorithm can improve detection performance and decrease false alarm probability.
Key words: spectral unmixing, support vector data description (SVDD), anomaly detection
CLC Number:
TP751.1
CHENG Bao-zhi, ZHAO Chun-hui, WANG Yu-lei. SVDD Algorithm with Spectral Unmixing for Anomaly Detection in Hyperspectral Images[J]. Journal of Applied Sciences, 2012, 30(1): 82-88.
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URL: https://www.jas.shu.edu.cn/EN/10.3969/j.issn.0255-8297.2012.01.013
https://www.jas.shu.edu.cn/EN/Y2012/V30/I1/82