Land/Water Discrimination and Land Cover Classification Using Multispectral Airborne LiDAR Data

Multispectral airborne Light Detection And Ranging (LiDAR) systems are currently available. Optech Titan is an example of these systems, which acquires LiDAR point clouds at three independent wavelengths (1550, 1064 and 532 nm) from Earth’s surface. This dissertation aims to use the radiometric information (i.e., intensity) of the Optech Titan LiDAR data along with the geometric information (e.g., height) for land/water discrimination in coastal zones and land cover classification of urban areas. A set of point features based on elevation, intensity, and geometry was extracted and evaluated for land/water discrimination in coastal zones. In addition, an automated land/water discrimination approach based on seeded region growing algorithm was presented. Two data subsets were tested at Lake Ontario and Tobermory Harbour in Ontario, Canada. The elevation and geometry-based features achieved average overall accuracies of 72.8% - 83.3% and 69.9% -74.4%, respectively, while the intensity-based features achieved an average overall accuracy of 59.0% - 63.4%. The region growing method achieved an average overall accuracy of more than 99%, and the automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength. A hierarchal point-based classification approach was presented for land cover classification of urban areas. The collected point clouds at the three wavelengths were first merged and three intensity values were estimated for each LiDAR point, followed by three-level classification approach. First, a ground filtering method was applied to separate non-ground from ground points. Second, three normalized difference vegetation indices (NDVIs) were computed, followed by NDVIs’ histograms construction. A multivariate Gaussian decomposition (MVGD) was then used to divide those histograms into buildings or trees from non-ground and roads or grass from ground points. Third, classes such as power lines, swimming pools and different types of trees were labeled based on their spectral characteristics. Three data subsets were tested representing different complexity of urban areas in Oshawa, Ontario, Canada. It is shown that the presented approach has achieved an overall accuracy up to 93.0%, which increased to more than 99% by considering the spatial coherence of the LiDAR point clouds.