Evaluation of Bayesian hyperspectral image segmentation with a discriminative class learning

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

Authors

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

Conference proceedings International

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

Pages: 3810-3813

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Barcelona, SPAIN, JUL 23-27, 2007

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 16 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4