Managing the spectral-spatial mix in context classification using Markov random fields

X. Jia; J. A. Richards

doi:10.1109/LGRS.2008.916076

Managing the spectral-spatial mix in context classification using Markov random fields

X. Jia^*, J. A. Richards

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

60 Citations (Scopus)

Abstract

A straightforward method is presented for determining the most appropriate weighting of the spectral and spatial contributions in the Markov random field approach to context classification. The spectral and spatial components are each normalized to fall in the range (0,1) after which the appropriate value for the weighting coefficient can determined simply, guided by an assessment of the importance of the spatial contribution. Experimental results are presented using an artificial data set and real data recorded by the Landsat Thematic Mapper and Airborne Visible/Infrared Imaging Spectrometer.

Original language	English
Article number	4472912
Pages (from-to)	311-314
Number of pages	4
Journal	IEEE Geoscience and Remote Sensing Letters
Volume	5
Issue number	2
DOIs	https://doi.org/10.1109/LGRS.2008.916076
Publication status	Published - Apr 2008

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10.1109/LGRS.2008.916076

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abstract = "A straightforward method is presented for determining the most appropriate weighting of the spectral and spatial contributions in the Markov random field approach to context classification. The spectral and spatial components are each normalized to fall in the range (0,1) after which the appropriate value for the weighting coefficient can determined simply, guided by an assessment of the importance of the spatial contribution. Experimental results are presented using an artificial data set and real data recorded by the Landsat Thematic Mapper and Airborne Visible/Infrared Imaging Spectrometer.",

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Managing the spectral-spatial mix in context classification using Markov random fields

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