Automatic object classification according to 3d-lidar data based on single-photon counting technology

Abstract

Subject of Research. We compare the effectiveness of the procedure for automatic object classification according to 3d-lidar data, built on the basis of single-photon counting technology, with the efficiency of the classification procedure with the usage of the conventional linear lidar data. Lidars based on the single-photons counting technology (SPCT) significantly exceed the ordinary linear lidars for a whole range of target parameters, including: spatial resolution - by more than an order of magnitude; the performance of the land surface scanning process - by an order of magnitude; by weight and size indicators - few times less. Unlike linear lidars, each laser point in the case of a SPCT-lidar is described not only by its coordinates and the intensity of the reflected signal, but by coordinates and an additional data block that characterizes the surface relief of the probed object in the direction of the probing photon flux. The presence of this additional data block for each laser point makes it possible to consider the received images as 3d-images, that simplifies the solution of not only the photogrammetric problem, but also the task of automatic objects classification on SPCT-image. Method. We consider the automatic object classification task solution based on SPCT-data with the use of the machine learning methods (XGBoost and multilayered neural networks - ANN). Main Results. The results of the numerical simulation carried out within the framework of the present study showed that, other things being equal, the efficiency of solving the classification problem based on SPCT-data, in practically important cases, increases up to 20% compared with the case where the linear-lidar data was used. Practical Relevance. The obtained results can be used in the design of the mobile SPCT-lidars enabling the detection and classification of objects on the Earth's surface in real time with high reliability.