6 July 2017 Spatial–spectral hyperspectral classification using local binary patterns and Markov random fields
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Abstract
Local binary patterns (LBPs) have been extensively used to yield spatial features for the classification of general imagery, and a few recent works have applied these patterns to the classification of hyperspectral imagery. Although the conventional LBP formulation employs only the signs of differences between a central pixel and its surrounding neighbors, it has been recently demonstrated that the difference magnitudes also possess discriminative information. Consequently, a sign-and-magnitude LBP is proposed to provide a spatial–spectral class-conditional probability for a Bayesian maximum
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Zhen Ye, Zhen Ye, James E. Fowler, James E. Fowler, Lin Bai, Lin Bai, } "Spatial–spectral hyperspectral classification using local binary patterns and Markov random fields," Journal of Applied Remote Sensing 11(3), 035002 (6 July 2017). https://doi.org/10.1117/1.JRS.11.035002 . Submission: Received: 21 January 2017; Accepted: 7 June 2017
Received: 21 January 2017; Accepted: 7 June 2017; Published: 6 July 2017
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