LIDAR Image Processing with Progressive Morphological Filtering

Abstract

High-resolution digital terrain models (DTMs) are essential for many topographic applications and LIDAR (Light Detection and Ranging) is one of the latest optical remote sensing technologies that used to generate DTM. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements with irregular spacing. In order to generate a DTM, measurements from unwanted features such as trees, vehicles have to be classified and removed. In this study, a progressive morphological filtering and its parametric performance in removing unwanted LIDAR measurements are studied. Numerical experiments show that the progressive morphological filter is more effective than the traditional morphological filter. LIDAR data's DTM generation. In this study, a progressive morphological filtering code based on Matlab (3) has been developed to remove unwanted LIDAR measurements and a parametric study is conducted to understand the effects of filter parameters. By selecting appropriate parameters, the measurements of unwanted objects were removed, while wanted measurements could be preserved.