Evaluation of Bayesian hyperspectral image segmentation with a discriminative class learning

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

Autores

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

Ata de Conferência Internacional

Título: IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET

Páginas: 3810-3813

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Barcelona, SPAIN, JUL 23-27, 2007

Indexação

ISI Web of Knowledge - 9 Citações

Scopus - 16 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

Outras Informações

ID Authenticus: P-004-DF4

DOI: 10.1109/igarss.2007.4423673

Abstract (EN): A Bayesian segmentation approach for hyperspectral images is introduced in this paper. The method improves the classification performance of discriminative classifiers by adding contextual information in the form of spatial dependencies. The technique herein presented builds the class densities based on Fast Sparse Multinomial Logistic Regression and enforces spacial continuity by adopting a Multi-Level Logistic Markov-Gibs prior. State-of-art performance of the proposed approach is illustrated in a set of experimental comparisons with recently introduced hyperspectral classification/segmentation methods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 4