Point-Cloud Class Separability: Identifying the Most Discriminative Features

The global availability of dense point-clouds provides the potential to better assess changes in our dynamic world, particularly environmental changes and natural hazards. A core step to make use of modern point-clouds is to have a reliable classification and identify features of importance for a successful classification. However,  the quality of classification is affected by both the classifier and the complexity of the features which describe the classes. To address the limitations of classification performance, we attempt to answer the question: To what extent can a classifier learn the separation into different classes based on the available features in a given training dataset?

We compare several measures of class separability to assess the descriptive value of each feature. A ranked list is generated that includes all individual features as well as all possible combinations within specific groups. Selecting high-ranked features based on their descriptive value allows us to summarize datasets without losing essential information about the individual classes. This is an important step in processing existing training data or in setting priorities for future data collection.

In our application experiments, we compare geometric and echo-based features of lidar point-clouds to obtain the most useful sets of features for separating ground and vegetation points into their respective classes. Different scenarios of suburban and natural areas are studied to collect various insights for different classification tasks. In addition, we group features based on various attributes such as acquisition or computational cost and evaluate the benefits of these efforts in terms of a possible better classification result.