Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

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

[+] Author Affiliations
Li Ni

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China

Lianru Gao

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Shanshan Li

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

Jun Li

Sun Yat-sen University, School of Geography and Planning, Guangdong Key Laboratory for Urbanization and Geo-Simulation, Guangzhou 510275, China

Bing Zhang

Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Key Laboratory of Digital Earth Science, No. 9 Dengzhuang South Road, Beijing 100094, China

J. Appl. Remote Sens. 8(1), 085089 (Oct 17, 2014). doi:10.1117/1.JRS.8.085089
History: Received April 18, 2014; Revised September 18, 2014; Accepted September 23, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Topics

LIDAR

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", J. Appl. Remote Sens. 8(1), 085089 (Oct 17, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085089


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