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IBPRIA
2007
Springer

Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning

10 years 10 months ago
Bayesian Hyperspectral Image Segmentation with Discriminative Class Learning
Abstract. This paper presents a new Bayesian approach to hyperspectral image segmentation that boosts the performance of the discriminative classifiers. This is achieved by combining class densities based on discriminative classifiers with a Multi-Level Logistic Markov-Gibs prior. This density favors neighbouring labels of the same class. The adopted discriminative classifier is the Fast Sparse Multinomial Regression. The discrete optimization problem one is led to is solved efficiently via graph cut tools. The effectiveness of the proposed method is evaluated, with simulated and real AVIRIS images, in two directions: 1) to improve the classification performance and 2) to decrease the size of the training sets.
Janete S. Borges, José M. Bioucas-Dias, And
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where IBPRIA
Authors Janete S. Borges, José M. Bioucas-Dias, André R. S. Marçal
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