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- W2117884915 abstract "The general objective of anomaly detection (AD) in hyperspectral imagery is to detect full-pixel targets. To meet this purpose, the global AD methods can achieve more reliable results than the local methods in terms of time and accuracy. The kernel-based Support Vector Data Description (SVDD) has recently received great attention in the hyperspectral AD applications. This paper presents a global SVDD-based method for autonomous full-pixel AD. The method consists of three steps: clustering, background modeling, and autonomous AD. Experimental results on a hyperspectral dataset show the superiority of the proposed method comparing to the global-based SVDD method." @default.
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- W2117884915 date "2011-07-01" @default.
- W2117884915 modified "2023-09-27" @default.
- W2117884915 title "A fast-adaptive support vector method for full-pixel anomaly detection in hyperspectral images" @default.
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- W2117884915 doi "https://doi.org/10.1109/igarss.2011.6049461" @default.
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