IEEE Trans Image Process - A novel technique for subpixel image classification based on support vector machine.

Tópicos

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Resumo

This paper presents a novel support vector machine classifier designed for sub-pixel 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 (F2SVM) classifier that can: i) process fuzzy information given as input to the classification algorithm for modeling the sub-pixel information in the learning phase of the classifier, and ii) 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 strategies (FOAO). These strategies generalize to the fuzzy case techniques based on ensembles of binary classifiers used for addressing multicategory problems in crisp classification problems. The effectiveness of the proposed F2SVM classifier is tested on three problems related to image classification in presence of mixed pixels having different characteristics. Experimental results confirm the validity of the proposed sub-pixel classification method.

Resumo Limpo

paper present novel support vector machin classifi design subpixel imag classif pixelspectr unmix propos classifi general properti svms identif model abund class mix pixel use fuzzi logic result definit fuzzyinput fuzzyoutput support vector machin fsvm classifi can process fuzzi inform given input classif algorithm model subpixel inform learn phase classifi ii provid fuzzi model classif result allow relat manytoon class pixel present binari fsvm can address multicategori problem accord two strategi fuzzi oneagainstal foaa fuzzi oneagainston strategi foao strategi general fuzzi case techniqu base ensembl binari classifi use address multicategori problem crisp classif problem effect propos fsvm classifi test three problem relat imag classif presenc mix pixel differ characterist experiment result confirm valid propos subpixel classif method

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