Abstract
Abstract. Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external information, such as spatial frequency characteristics or presume parametric mathematical models for surface fitting. A limitation on the height histogram filtering approach was identified from the literature review: outliers that lie within the ground elevation difference might not be detected. To overcome such a limitation, this paper proposes an adaptive alternative based on point cloud cell subdivision. Instead of computing a single histogram for the whole dataset, the method applies the filtering to smaller patches, in which the ground elevation difference can be ignored. A study area was filtered, and the results were compared quantitatively with two other methods implemented in both free and commercial software packages. The reference data was generated manually in order to provide useful quality measures. The experiment shows that none of the tested filters was able to reach a level of complete automation, therefore manual corrections by the operator are still necessary.
Highlights
Despite the robustness of state of the art of airborne LASER scanning (ALS) systems, and even with diligent data acquisition, the resulting point clouds may contain undesired measures due to external factors present in the scene
This paper provides a brief description of current approaches for outlier detection on point clouds
A straightforward variation of the height histogram filter was proposed, the cell histogram filter which aims at an adaptive solution to terrain relief
Summary
Despite the robustness of state of the art of airborne LASER scanning (ALS) systems, and even with diligent data acquisition, the resulting point clouds may contain undesired measures due to external factors present in the scene. Within the scope of ALS, the outliers (Figure 1) can be divided into positive and negative (Matkan et al, 2014): the positive ones consist of LASER returns from objects near the scanning system (Figure 1b), such as birds or small unmanned aircraft, for instance. In addition to those causes, Leslar et al (2010) mention that suspended particles in the atmosphere (snow and dust, for example) are possible sources of positive outliers. The atmospheric effects could interfere in some situations, in practice they are less likely to generate outliers since the data acquisition is usually planned to occur under reasonable weather conditions
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