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, James E. Fowler, 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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