Fusion of spectral and spatial information by a novel SVM classification technique

A novel context-sensitive semisupervised classification technique based on support vector machines is proposed. This technique aims at exploiting the SVM method for image classification by properly fusing spectral information with spatial- context information. This results in: i) an increased robustness to noisy training sets in the learning phase of the classifier; ii) a higher and more stable classification accuracy with respect to the specific patterns included in the training set; and iii) a regularized classification map. The main property of the proposed context sensitive semisupervised SVM (CS⁴VM) is to adaptively exploit the contextual information in the training phase of the classifier, without any critical assumption on the expected labels of the pixels included in the same neighborhood system. This is done by defining a novel context-sensitive term in the objective function used in the learning of the classifier. In addition, the proposed CS⁴VM can be integrated with a Markov random field (MRF) approach for exploiting the contextual information also to regularize the classification map. Experiments carried out on very high geometrical resolution images confirmed the effectiveness of the proposed technique.