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|Title:||An approach to classification of airborne laser scanning point cloud data in an urban environment|
|Publisher:||Taylor & Francis|
|Citation:||International journal of remote sensing, 2011, 32(24), pp. 9151-9169.|
|Abstract:||Digital topographic data, including detailed maps required for urban planning, are still unavailable in many parts of the world. Airborne laser scanning (ALS) has the unique ability to provide geo-referenced three-dimensional data useful for the mapping of urban features. This paper examines the performance of decision tree classifiers on two ALS datasets, collected in different seasons from different flying heights with different scanners using laser beams at different wavelengths - 1550 nm and 1064 nm - for the same study area. Classification was undertaken on the point clouds based on attributes derived from the TIN triangles attached to a point, as well as attributes of the individual points. Classification accuracies of 0.68 and 0.92 (kappa coefficient) could be achieved for the two datasets. Decision tree seems to be a classification method that is particularly suitable for GIS, as it can be converted to ‘if-then’ rules that can be implemented fully within a GIS environment. Grass and paved areas could be distinguished better using intensity from one dataset than the other, which could be related to the wavelengths of the lasers, and need to be explored further.|
|Rights:||© 2011 Taylor & Francis.|
|Description:||Full text currently not available owing to copyright restrictions. This article is embargoed until Nov 2012.|
|Appears in Collections:||Published Articles, Dept. of Geography|
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