Classification of hyperspectral data by continuation semi-supervised SVM

This paper presents a semi-supervised technique for the solution of ill-posed classification problems in remote sensing applications. The proposed technique is based on semi- supervised support vector machines (S³VMs) implemented in the primal formulation of the learning problem. In particular, a global optimization algorithm, based on the continuation method, is adopted in the learning phase of the classifier according to an iterative learning procedure. The use of this algorithm can result in a better approximation to the global minimum of the associated cost function. Experimental results, obtained on hyperspectral remote sensing images, point out the advantages and the limitation of the proposed continuation S³VM (cS³VMs) with respect to other implementations of S³VMs.