Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our p...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2013-02, Vol.51 (2), p.844-856
Main Authors: Jun Li, Bioucas-Dias, J. M., Plaza, A.
Format: Article
Language:English
Subjects:
Active learning (AL)
Applied geophysics
Complexity theory
discriminative random fields (DRFs)
Earth sciences
Earth, ocean, space
Exact sciences and technology
hyperspectral image classification
Hyperspectral imaging
Inference algorithms
Internal geophysics
loopy belief propagation (LBP)
Markov random fields (MRFs)
spectral-spatial analysis
Training
Vectors
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Summary:In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2012.2205263