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Sensors 2017, 17(5), 958; doi:10.3390/s17050958

Multispectral LiDAR Data for Land Cover Classification of Urban Areas

Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada
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Academic Editor: Vittorio M. N. Passaro
Received: 19 February 2017 / Revised: 14 April 2017 / Accepted: 24 April 2017 / Published: 26 April 2017
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Abstract

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood classifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. View Full-Text
Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction
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MDPI and ACS Style

Morsy, S.; Shaker, A.; El-Rabbany, A. Multispectral LiDAR Data for Land Cover Classification of Urban Areas. Sensors 2017, 17, 958.

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