Transductive SVMs for semisupervised classification of hyperspectral data

This paper presents transductive support vector machines (TSVMs) for the semisupervised classification of hyperspectral remote sensing images. On the basis of the analysis of TSVMs recently introduced in the machine learning literature and of the properties of hyperspectral classification problems, a specific TSVM algorithm is proposed to alleviate the Hughes phenomenon in a nonparametric and kernel-based classification framework. The extension of the proposed technique to multiclass cases is also discussed. Experimental results obtained on a real hyperspectral image point out that when small-size training data are available, the proposed TSVMs outperform standard inductive support vector machines (ISVMs).