Bayesian hyperspectral image segmentation with discriminative class learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2007

Autores

borges, js (Autor) Outra; bioucas-dias, jm (Autor) Outra; marcal, ars (Autor) FCUP

Ata de Conferência Internacional

Título: Pattern Recognition and Image Analysis (Part 1)

Páginas: 22-29

IbPRIA 2007 - Iberian Conference on Pattern Recognition and Image Analysis

Girona, Spain, 6 a 8 de Junho de 2007

Indexação

ISI Web of Knowledge - 5 Citações

Scopus - 6 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8