Abstract:
Hyperspectral sensors accurately sample the spectral signatures of different land covers, thus allowing an effective discrimination of cover classes or ground materials. ...Show MoreMetadata
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.
Published in: 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
Date of Conference: 14-16 June 2010
Date Added to IEEE Xplore: 04 October 2010
ISBN Information: