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- W2116073259 abstract "Hyperspectral sensors accurately sample the spectral signatures of different land covers, thus allowing an effective discrimination of cover classes or ground materials. However, addressing a supervised classification problem with hundreds of features involves critical small-sample size issues. Moreover, traditional hyperspectral-image classifiers are usually noncontextual. In this paper, a novel method is proposed, that is based on the integration of the support vector machine (SVM) and Markov randomfield (MRF) approachesto classification and is aimed at a rigorous contextual generalization of SVMs. A reformulation of the Markovian minimum-energy rule is introduced and is analytically proven to be equivalent to the application of an SVM in a suitably transformed space. The internal parameters of the method are automatically optimized by extending recently developed techniques based on the Ho-Kashyap and Powell's numerical algorithms and the proposed classifier is also combined with the recently proposed band-extraction approach to feature reduction." @default.
- W2116073259 created "2016-06-24" @default.
- W2116073259 creator A5012936131 @default.
- W2116073259 date "2010-06-01" @default.
- W2116073259 modified "2023-09-25" @default.
- W2116073259 title "A Markovian generalization of support vector machines for contextual supervised classification of hyperspectral images" @default.
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- W2116073259 doi "https://doi.org/10.1109/whispers.2010.5594967" @default.
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