DEMs (digital elevation models) are of critical importance in different areas such as land use planning, infrastructural project management, soil science, hydrology and flow direction studies. Across greater spatial scales, their usage is the key for contouring topographic and relief maps. A DEM represents the bare surface, eliminating all natural and artificial features, while the digital surface model (DSM) captures both natural and artificial features of the environment. DSM is of significant interest for applications such as environmental planning, map updating, or building detection. Ground filtering is the removal of the points belonging to the above-ground objects in order to retrieve ground points to be used in generating DEM. DEM can be effectively obtained from LIDAR or digital photogrammetry. Lidar point clouds have great success in representing the objects they belong to; but since the Lidar data acquisition is still a costly process, using point clouds generated by the photogrammetric process to produce DSM is a reasonable alternative. Since DSM represents the information of surface of the land objects and is also affected by ground slope, it cannot be useful lonely for interpreting the data; therefore, to make optimal use of it, a distinction is required between the land and non-land pixels. On this basis, due to the large volume of the high-resolution images and with regard to complex urban structure, a fast yet simple and accurate method is desirable.
Material & Methods
Based on the filtering algorithms, the provided digital surface model is classified into ground and off-ground pixels. For all the off-ground pixels, the closest ground point is assumed to be the relevant low point, thus, through the height difference of the off-ground point with the assigned ground point, the so-called normalized height is computed. However, most of the filtering algorithms are mainly developed to filter Lidar data and will require the adjustment of a number of complex parameters to achieve high accuracy. At the same time, the processing time, degree of effectiveness in different scenes, and degree of automation of these methods are also important. Scene details and topographical complexity, for example in urban areas, make the filtering process even more challenging. For optimal results, users should try to adjust various parameters until they find the desired filtering result, which is a time-consuming and costly process. Due to the lack of a comprehensive study on the efficiency, automation, and computational complexity of different filtering methods on the points cloud obtained from photogrammetry, in this study, different and most widely used algorithms in this field of study were compared with each other. The studied methods were analyzed in terms of class filtering quality, processing time (execution time), scene complexity, and number of algorithm parameters (indicating the degree of user involvement in data processing to determine the amount of automation). Results of this analysis can be useful in order to better understanding the performance of filtering methods on the DSM obtained from high resolution images (dense point clouds from aerial and UAV images). In addition, it can be suitable for different users according to the parameters of time, hardware, scene type, and output accuracy.
Result & Discussion
Ground filtering is essential for DEM generation. In this paper, for ground filtering, at first, a suitable algorithm was selected and, after setting the initial parameters, they were applied to the point clouds. Comparing the obtained results, it can be seen that in the building class with sloping roofs, Morph and ATIN methods performed better, but in buildings with flat roofs, only Morph method had good accuracy. In the mono-tree class, the Morph and ATIN methods in Metashape software were able to perform the separation well, and in the tree row class, both methods performed well. The ATIN method in Metashape software was able to differentiate the road class more accurately than other methods. It also performed well in the river class. Therefore, according to the results of this study, if the goal is to identify high tolls in urban areas, due to the lower computational cost of the Morph method than the ATIN method, the Morph method is recommended. But if the goal is to produce good quality DTM, the ATIN method will be the priority.
In this research, ATIN, ETEW, MLS, MORPH1D, and MORPH2D algorithms for land extraction were evaluated. Thus, first the algorithms were examined on the test data and, then, the results were analyzed with the ground true images. In this study, five filtering methods were examined and compared on three images of urban areas, which included various natural and human-made features, including streets, trees, and buildings. The data were related to the digital aerial imagery taken by Intergraph/ZI DMC sensor in Vaihingen city, Germany. DSM data sets were defined on the grid with the ground resolution of 9°cm. Comparing the results of all the three data sets, it can be seen that the difference in accuracy between the one- and two-dimensional morphology algorithms was very small and they had similar performance. In terms of processing time, the ATIN method had longer execution time than other methods and the ETEW method had shorter execution time than other algorithms. Also, the number of algorithm parameters indicated the degree of user participation in data processing. Therefore, due to the point that the ETEW algorithm had fewer parameters, its degree of automation was higher than other algorithms. Comparing and reviewing the results obtained from the test data demonstrated that MLS and ETEW algorithms had the lowest efficiency in the urban area. On the other hand, in features such as buildings with sloping roofs, single trees, and tree rows, two ATIN and Morph algorithms provided favorable results. According to the obtained results, the suitable algorithm was Morph algorithm for flat-roofed buildings and ATIN algorithm for road and parking. In general, it is recommended to use the Morph algorithm for urban and small areas due to time savings and less effective parameters.