Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas

Li Ni; Lianru Gao; Shanshan Li; Jun Li; Bing Zhang

doi:10.1117/1.JRS.8.085089

17 October 2014 Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas

Li Ni, Lianru Gao, Shanshan Li, Jun Li, Bing Zhang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 8, Issue 1, 085089 (October 2014). https://doi.org/10.1117/1.JRS.8.085089

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.

Citation Download Citation

Li Ni, Lianru Gao, Shanshan Li, Jun Li, and Bing Zhang "Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas," Journal of Applied Remote Sensing 8(1), 085089 (17 October 2014). https://doi.org/10.1117/1.JRS.8.085089

Published: 17 October 2014

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