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Integration of high density airborne lidar and high spatial resolution image for landcover classification

Abdul Rahman, Muhammad Zulkarnain and Wan Kadir, Wan Hazli and Rasib, Abd. Wahid and Ariffin, Azman and Razak, Khamarrul Azahari (2013) Integration of high density airborne lidar and high spatial resolution image for landcover classification. In: International Geoscience and Remote Sensing Symposium, Melbourne, Australia 21-26 July 2012.

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This paper discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is between 50 and 100 points per m2. Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only.

Item Type:Conference or Workshop Item (Paper)
Subjects:G Geography. Anthropology. Recreation
Divisions:Geoinformation and Real Estate
ID Code:38078
Deposited By: Liza Porijo
Deposited On:11 May 2014 07:23
Last Modified:25 Sep 2017 04:02

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