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18 October 2005 A novel transductive SVM for semisupervised classification of remote sensing images
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Abstract
This paper introduces a semisupervised classification method, which exploits both labeled and unlabeled samples, for addressing "ill-posed" problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular Transductive SVMs (TSVMs). We propose a novel modified TSVM classifier designed for the analysis of "ill-posed" remotesensing problems. In particular, the proposed technique: i) is based on a novel transductive procedure that exploits a weighting strategy for the unlabeled patterns based on a time-dependent criterion; ii) is developed also for multiclass cases; and iii) addresses the model-selection problem with lack of test/validation sets. Experimental results confirm the effectiveness of the proposed method on a set of "ill-posed" remote-sensing classification problems representing different operative conditions.
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Mingmin Chi and Lorenzo Bruzzone "A novel transductive SVM for semisupervised classification of remote sensing images", Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 59820G (18 October 2005); https://doi.org/10.1117/12.628862
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