17 October 2014 Edge-constrained Markov random field classification by integrating hyperspectral image with LiDAR data over urban areas
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J. of Applied Remote Sensing, 8(1), 085089 (2014). doi: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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Li Ni, Lianru Gao, Shanshan Li, Jun Li, 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). http://dx.doi.org/10.1117/1.JRS.8.085089
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KEYWORDS
LIDAR

Image classification

Magnetorheological finishing

Hyperspectral imaging

Fusion energy

Data modeling

Roads

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