Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal

This paper addresses classification of hyperspectral remote sensing images with kernel-based methods defined in the framework of semisupervised support vector machines (S
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VMs). In particular, we analyzed the critical problem of the nonconvexity of the cost function associated with the learning phase of S
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VMs by considering different (S
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VMs) techniques that solve optimization directly in the primal formulation of the objective function. As the nonconvex cost function can be characterized by many local minima, different optimization techniques may lead to different classification results. Here, we present two implementations, which are based on different rationales and optimization methods. The presented techniques are compared with S
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VMs implemented in the dual formulation in the context of classification of real hyperspectral remote sensing images. Experimental results point out the effectiveness of the techniques based on the optimization of the primal formulation, which provided higher accuracy and better generalization ability than the S
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VMs optimized in the dual formulation