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Journal of Applied Remote Sensing

Spatial–spectral hyperspectral classification using local binary patterns and Markov random fields
Author(s): Zhen Ye; James E. Fowler; Lin Bai
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Paper 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

Paper Details

Date Published: 6 July 2017
PDF: 14 pages
J. Appl. Rem. Sens. 11(3) 035002 doi: 10.1117/1.JRS.11.035002
Published in: Journal of Applied Remote Sensing Volume 11, Issue 3
Show Author Affiliations
Zhen Ye, Chang'an Univ. (China)
James E. Fowler, Mississippi State Univ. (United States)
Lin Bai, Chang'an Univ. (China)

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