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Improved hierarchical optimization-based classification of hyperspectral images using shape analysis

Abstract : A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.
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Contributor : Yuliya Tarabalka Connect in order to contact the contributor
Submitted on : Friday, September 7, 2012 - 12:09:20 PM
Last modification on : Saturday, January 27, 2018 - 1:31:38 AM
Long-term archiving on: : Friday, December 16, 2016 - 10:50:18 AM


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  • HAL Id : hal-00729038, version 1



Yuliya Tarabalka, James Tilton. Improved hierarchical optimization-based classification of hyperspectral images using shape analysis. IEEE IGARSS - International Geoscience and Remote Sensing Symposium, IEEE, Jul 2012, Munich, Germany. ⟨hal-00729038⟩



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