SUPERVISED CLASSIFICATION AND ITS REPEATABILITY FOR POINT CLOUDS FROM DENSE VHR TRI-STEREO SATELLITE IMAGE MATCHING USING MACHINE LEARNING
Image matching of aerial or satellite images and Airborne Laser Scanning (ALS) are the two main techniques for the acquisition of geospatial information (3D point clouds), used for mapping and 3D modelling of large surface areas. While ALS point cloud classification is a widely investigated topic, there are fewer studies related to the image-derived point clouds, even less for point clouds derived from stereo satellite imagery. Therefore, the main focus of this contribution is a comparative analysis and evaluation of a supervised machine learning classification method that exploits the full 3D content of point clouds generated by dense image matching of tri-stereo Very High Resolution (VHR) satellite imagery. The images were collected with two different sensors (Pléiades and WorldView-3) at different timestamps for a study area covering a surface of 24 km 2, located in Waldviertel, Lower Austria. In particular, we evaluate the performance and precision of the classifier by analysing the variation of the results obtained after multiple scenarios using different training and test data sets. The temporal difference of the two Pléiades acquisitions (7 days) allowed us to calculate the repeatability of the adopted machine learning algorithm for the classification. Additionally, we investigate how the different acquisition geometries (ground sample distance, viewing and convergence angles) influence the performance of classifying the satellite image-derived point clouds into five object classes: ground, trees, roads, buildings, and vehicles. Our experimental results indicate that, in overall the classifier performs very similar in all situations, with values for the F1-score between 0.63 and 0.65 and overall accuracies beyond 93%. As a measure of repeatability, stable classes such as buildings and roads show a variation below 3% for the F1-score between the two Pléiades acquisitions, proving the stability of the model.