Classification of Airborne Multispectral Lidar Point Clouds for Land Cover Mapping

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2018-06, Vol.11 (6), p.2068-2078
• Main Authors: Ekhtari, Nima, Glennie, Craig, Fernandez-Diaz, Juan Carlos
• Format: Article
• Language: English
• Subjects: Accuracy; Airborne lasers; Buildings; Classification; Coefficients; Computer applications; Data; Detection; Distance measurement; Earth; Image classification; Imagery; Land cover; Laser radar; Lasers; Lidar; light detection and ranging (lidar); Mapping; multispectral lidar; point cloud; Soil; Support vector machines; Three dimensional models; Three-dimensional displays; Titan laser scanner; Wavelengths
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2018.2835483

Airborne light detection and ranging (lidar) data are widely used for high-resolution land cover mapping. The lidar elevation data are typically used as complementary information to passive multispectral or hyperspectral imagery to enable higher land cover classification accuracy. In this paper, we examine the capabilities of a recently developed multispectral airborne laser scanner, manufactured by Teledyne Optech, for direct classification of multispectral point clouds into ten land cover classes including grass, trees, two classes of soil, four classes of pavement, and two classes of buildings. The scanner, Titan MW, collects point clouds at three different laser wavelengths simultaneously, opening the door to new possibilities in land cover classification using only lidar data. We show that the recorded intensities of laser returns together with spatial metrics calculated from the three-dimensional (3D) locations of laser returns are sufficient for classifying the point cloud into ten distinct land cover classes. Our classification methods achieved an overall accuracy of 94.7% with a kappa coefficient of 0.94 using the support vector machine (SVM) method to classify single-return points and an overall accuracy of 79.7% and kappa coefficient of 0.77 using a rule-based classifier on multireturn points. A land cover map is then generated from the classified point cloud. We show that our results outperform the common approach of rasterizing the point cloud prior to classification by ~4% in overall accuracy, 0.04 in kappa coefficient, and by up to 16% in commission and omission errors. This improvement however comes at the price of increased complexity and computational burden.