Journal
JOURNAL OF APPLIED REMOTE SENSING
Volume 8, Issue -, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JRS.8.085089
Keywords
hyperspectral image; LiDAR data; Markov random field; support vector machine; classification
Funding
- National Natural Science Foundation of China [41325004, 41401394, 41301383]
- National High Technology Research and Development Programme of China [2012AA12A301]
- NSF-funded Center for Airborne Laser Mapping
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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. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
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