Name
Title | ||
|---|---|---|
A Markovian generalization of support vector machines for contextual supervised classification of hyperspectral images |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WHISPERS.2010.5594967 | WHISPERS |
Keywords | Field | DocType |
markov processes,feature extraction,geophysical image processing,image classification,random processes,support vector machines,terrain mapping,ho-kashyap numerical algorithm,markov randomfield,markovian generalization,markovian minimum-energy rule,powell numerical algorithm,band-extraction,contextual supervised image classification,feature reduction,hyperspectral image classification,hyperspectral sensor,land cover,spectral signature,support vector machine,markov random fields,band extraction,hyperspectral imaging,accuracy,pixel,kernel | Kernel (linear algebra),Markov process,Pattern recognition,Support vector machine,Markov chain,Hyperspectral imaging,Feature extraction,Artificial intelligence,Contextual image classification,Classifier (linguistics),Mathematics,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4244-8907-7 | 4 | 0.52 |
References | Authors | |
6 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Moser | 1 | 919 | 76.92 |
| Serpico, S.B. | 2 | 560 | 48.52 |