| dc.description.abstract | In the dissertation, ground points are selected and building regions are detected from airborne Light Detection and Ranging (LiDAR) data. In the first part of the paper the system, error sources, and the data features of the airborne LiDAR are described after which several major filtering algorithms and their characteristics are reviewed and compared. A novel slope-based climbing-and-sliding (CAS) method is developed to select ground points from airborne LIDAR data which takes into consideration the features of height, slope and area of the region of bare earth. In the proposed method not only is a local search performed but the merits of a global treatment are preserved. A scheme is proposed to improve the efficiency and accuracy where the ground points in the initial surface model are selected based on a novel pseudo-grid. After this a back selection step is performed to retrieve detailed terrain features. Considering that bridges have tended to be misclassified as parts of the ground, an additional detection step is included to remove bridge points.
In the second part of the dissertation, building regions in the set of above-ground points are detected. Prior knowledge of such things as the height, size, and area of the buildings, is employed to first remove extraneous points or regions and to detect building candidates. Building and tree are two main dominate classes in the candidates. Based on the assumption that buildings roofs are piece-wise continuous, ten region-based textures based on directional slope difference are analyzed. At the last, an unsupervised classification is performed for the building detection.
The filtering error of the generated DEM is evaluated, as well as the test of parameter sensitivity. The processing results are quantitatively compared with several recognized counterparts in the literature. The presentation of the terrain features is also analyzed. The experimental results demonstrate that the proposed scheme can be applied to diverse terrain types. To validate the detection scheme, two data sets including urban and rural areas in Taiwan are tested. The experimental results indicate that two features of homogeneity and probability of a small slope difference preserve most information and thus are most suitable for the detection. | en_US |