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Journal of Applied Remote Sensing

Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas
Author(s): Li Ni; Lianru Gao; Shanshan Li; Jun Li; Bing Zhang
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Paper Abstract

This paper proposes an edge-constrained Markov random field (EC-MRF) method for accurate land cover classification over urban areas using hyperspectral image and LiDAR data. EC-MRF adopts a probabilistic support vector machine for pixel-wise classification of hyperspectral and LiDAR data, while MRF performs as a postprocessing regularizer for spatial smoothness. LiDAR data improve both pixel-wise classification and postprocessing result during an EC-MRF procedure. A variable weighting coefficient, constrained by a combined edge extracted from both hyperspectral and LiDAR data, is introduced for the MRF regularizer to avoid oversmoothness and to preserve class boundaries. The EC-MRF approach is evaluated using synthetic and real data, and results indicate that it is more effective than four similar advanced methods for the classification of hyperspectral and LiDAR data.

Paper Details

Date Published: 17 October 2014
PDF: 13 pages
J. Appl. Remote Sens. 8(1) 085089 doi: 10.1117/1.JRS.8.085089
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Li Ni, Institute of Remote Sensing and Digital Earth (China)
The Univ. of Chinese Academy of Sciences (China)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Shanshan Li, Institute of Remote Sensing and Digital Earth (China)
Jun Li, Sun Yat-Sen Univ. (China)
Bing Zhang, Institute of Remote Sensing and Digital Earth (China)


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