Application of convolutional neural networks for low vegetation filtering from data acquired by UAVs

Abstract

Abstract The main advantage of using unmanned aerial vehicles (UAVs) is the relatively low cost of collecting data, especially when using photogrammetry on images of relatively small areas. Additionally, they have high operational flexibility and the results have a high spatial and temporal resolution. To further facilitate the use of UAVs in photogrammetry, we developed an algorithm to filter out points that indicate areas covered in low vegetation (grass, crops) from the generated point cloud. This paper presents a three-layer filtering algorithm based on convolutional neural networks (CNNs) created for this specific purpose. The modular structure of the algorithm makes it easy to expand on and improve. The proposed solution allows errors in the height of digital elevation model (DEM) points caused by the influence of vegetation to be reduced by as much as 60–70% in relation to height errors from the raw data of high grass. At the same time, the solution presented here is practical for low grass because it does not weaken the model. The algorithm significantly reduces the errors in the DEM, as well as the products derived from the DEM.