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http://dx.doi.org/10.7848/ksgpc.2011.29.4.429

Land cover classification using LiDAR intensity data and neural network  

Minh, Nguyen Quang (Department of Surveying and Mapping, Hanoi University of Mining and Geology)
Hien, La Phu (GeoSpatial Image Processing & Remote Sensing Lab, Konkuk University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.29, no.4, 2011 , pp. 429-438 More about this Journal
Abstract
LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.
Keywords
LiDAR; Digital Elevation Model; Digital Surface Model; Neural Network Classification;
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