Classification of Forest LiDAR Data Using Deep Learning Pipeline Algorithm and Geometric Feature Analysis

Abstract

This paper adapts the deep learning pipeline algorithm based on the Multi-Layer Perceptron (MLP) Neural Network to automatically classify the forest Light Detection And Ranging (LiDAR) point cloud. To achieve this, the Machine Learning (ML) algorithm parameters such as input layer elements, number of hidden layers, activation functions, and alpha value are optimized to achieve the best possible performance. Regarding the important role of the geometric features in the input layer, most of the suggested features in the literature are analyzed to employ the more effective ones in the algorithm input layer. As a result, seven geometric features, in addition to the 3D coordinates of the point cloud, are chosen to represent the first algorithm layer. The proposed algorithm classifies the forest LiDAR point cloud into two classes: vegetation and terrain. The proposed approach was tested using two points of clouds, one of a flat area and the other of a mountain area. The results of using the suggested approach provide an accuracy score greater than 98%. The obtained result confirms the high efficiency of the proposed classification algorithm regarding the envisaged approaches in the literature. Finally, the next step is to generalize this approach to classify more complicated scenes as urban areas.