Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 4, Issue 1, Pages 65-74Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2010.2069085
Keywords
Clustering; image classification; kernel methods; support vector machine (SVM); urban monitoring; very high resolution (VHR)
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Funding
- Swiss National Science Foundation [100012-113506, PBLAP2-127713/1]
- Spanish Ministry of Science and Innovation [TEC2006-13845, CSD2007-00018, AYA2008-05965-C04-03]
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This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.
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