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.

• Type: Conference
• Year: 2007
• Where: IBPRIA
• Authors: Janete S. Borges, José M. Bioucas-Dias, André R. S. Marçal
• Keywords: Discriminative Classifier, Hyperspectral Image Segmentation, IBPRIA 2007, Image, Image Analysis, Sparse Multinomial Regression