A Novel Technique for Subpixel Image Classification Based on Support Vector Machine

This paper presents a novel support vector machine classifier designed for subpixel image classification (pixel/spectral unmixing). The proposed classifier generalizes the properties of SVMs to the identification and modeling of the abundances of classes in mixed pixels by using fuzzy logic. This results in the definition of a fuzzy-input fuzzy-output support vector machine (F 2SVM) classifier that can: 1) process fuzzy information given as input to the classification algorithm for modeling the subpixel information in the learning phase of the classifier and 2) provide a fuzzy modeling of the classification results, allowing a relation many-to-one between classes and pixels. The presented binary F2SVM can address multicategory problems according to two strategies: the fuzzy one-against-all (FOAA) and the fuzzy one-against-one (FOAO) strategies. These strategies generalize to the fuzzy case techniques based upon ensembles of binary classifiers used for addressing multicategory problems ...