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http://dx.doi.org/10.7780/kjrs.2006.22.2.101

Rural Land Cover Classification using Multispectral Image and LIDAR Data  

Jang Jae-Dong (Department of Geomatics Sciences, Laval University)
Publication Information
Korean Journal of Remote Sensing / v.22, no.2, 2006 , pp. 101-110 More about this Journal
Abstract
The accuracy of rural land cover using airborne multispectral images and LEAR (Light Detection And Ranging) data was analyzed. Multispectral image consists of three bands in green, red and near infrared. Intensity image was derived from the first returns of LIDAR, and vegetation height image was calculated by difference between elevation of the first returns and DEM (Digital Elevation Model) derived from the last returns of LIDAR. Using maximum likelihood classification method, three bands of multispectral images, LIDAR vegetation height image, and intensity image were employed for land cover classification. Overall accuracy of classification using all the five images was improved to 85.6% about 10% higher than that using only the three bands of multispectral images. The classification accuracy of rural land cover map using multispectral images and LIDAR images, was improved with clear difference between heights of different crops and between heights of crop and tree by LIDAR data and use of LIDAR intensity for land cover classification.
Keywords
Land cover map; multispectral image; LIDAR; Vegetation height; LIDAR intensity; Maximum likelihood method;
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