Bayesian hyperspectral image segmentation with discriminative class learning

• Type: Article in International Conference Proceedings Book
• Date: 2007

Authors

borges, js (Author) Other; bioucas-dias, jm (Author) Other; marcal, ars (Author) FCUP

Conference proceedings International

Title: Pattern Recognition and Image Analysis (Part 1)

Pages: 22-29

IbPRIA 2007 - Iberian Conference on Pattern Recognition and Image Analysis

Girona, Spain, 6 a 8 de Junho de 2007

Indexing

ISI Web of Knowledge - 5 Citations

Scopus - 6 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-E53

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8