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Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields

1
IMEC-VisionLab, University of Antwerp, 2000 Antwerp, Belgium
2
IMEC-IPI, Ghent University, 9000 Gent, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 624; https://doi.org/10.3390/rs11060624
Received: 29 January 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
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Abstract

Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed pixels. Conventional classification techniques do not cope well with these problems. Thus, in addition to the spectral information, features were developed for a more complete description of the pixels, e.g., containing contextual information at the superpixel level or mixed pixel information at the subpixel level. This has encouraged an evolution of fusion techniques which use these myriad of multiple feature sets and decisions from individual classifiers to be employed in a joint manner. In this work, we present a flexible decision fusion framework addressing these issues. In a first step, we propose to use sparse fractional abundances as decision source, complementary to class probabilities obtained from a supervised classifier. This specific selection of complementary decision sources enables the description of a pixel in a more complete way, and is expected to mitigate the effects of small training samples sizes. Secondly, we propose to apply a fusion scheme, based on the probabilistic graphical Markov Random Field (MRF) and Conditional Random Field (CRF) models, which inherently employ spatial information into the fusion process. To strengthen the decision fusion process, consistency links across the different decision sources are incorporated to encourage agreement between their decisions. The proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward way. Experimental results conducted on two real hyperspectral images show superiority over several other approaches in terms of classification performance when very limited training data is available. View Full-Text
Keywords: hyperspectral unmixing; Markov random field; conditional random field; decision fusion; supervised classification hyperspectral unmixing; Markov random field; conditional random field; decision fusion; supervised classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Andrejchenko, V.; Liao, W.; Philips, W.; Scheunders, P. Decision Fusion Framework for Hyperspectral Image Classification Based on Markov and Conditional Random Fields. Remote Sens. 2019, 11, 624.

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