Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery

Abstract : A new method for spectral-spatial classification of hyperspectral images is proposed. The method is based on the integration of probabilistic classification within the hierarchical best merge region growing algorithm. For this purpose, preliminary probabilistic support vector machines classification is performed. Then, hierarchical step-wise optimization algorithm is applied, by iteratively merging regions with the smallest Dissimilarity Criterion (DC). The main novelty of this method consists in defining a DC between regions as a function of region statistical and geometrical features along with classification probabilities. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's vegetation area and compared with those obtained by recently proposed spectral-spatial classification techniques. The proposed method improves classification accuracies when compared to other classification approaches.
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Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium, Jul 2011, Vancouver, Canada. pp.3724-3727, 2011
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  • HAL Id : hal-00728528, version 1

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Yuliya Tarabalka, James Tilton. Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery. IEEE International Geoscience and Remote Sensing Symposium, Jul 2011, Vancouver, Canada. pp.3724-3727, 2011. 〈hal-00728528〉

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