期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 51, 期 2, 页码 844-856出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2012.2205263
关键词
Active learning (AL); discriminative random fields (DRFs); hyperspectral image classification; loopy belief propagation (LBP); Markov random fields (MRFs); spectral-spatial analysis
类别
资金
- European Community's Marie Curie Research Training Networks Programme [MRTN-CT-2006-035927]
- Portuguese Science and Technology Foundation [PEst-OE/EEI/LA0008/2011]
- Spanish Ministry of Science and Innovation (Calibration of Earth Observation Satellites in SPAIN (CEOS-SPAIN) project [AYA2011-29334-C02-02]
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.
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