Segmentering och klassificering av LiDAR-data
With numerous applications in both military and civilian life, the demand for accurate 3D models of real world environments increases rapidly. Using an airborne laser scanner for the raw data acquisition and robust methods for data processing, the researchers at the Swedish Defence Research Agency (FOI) in Linköping hope to fully automate the modeling process.The work of this thesis has mainly been focused on three areas: ground estimation, image segmentation and classification. Procedures have in each of these areas been developed, leading to a new algorithm for ground estimation, a number of segmentation methods as well as a full comparison of various decision values for an object based classification. The ground estimation algorithm developed has yielded good results compared to the method based on active contours previously elaborated at FOI. The computational effort needed by the new method has been greatly reduced compared to the former, as performance, particularly in urban areas, has been improved. The segmentation methods introduced have shown promising results in separating different types of objects. A new set of decision values and descriptors for the object based classifier has been suggested, which, according to tests, prove to be more efficient than the set p reviously used.