A Novel Filtering Algorithm for Bare-Earth Extraction From Airborne Laser Scanning Data Using an Artificial Neural Network

Abstract

The extraction of bare-earth points from airborne laser scanning (ALS) data and the generation of high-quality digital terrain models (DTMs) are important research challenges. In this study, a novel filtering algorithm based on artificial neural networks (ANNs) is proposed to extract bare-earth points from ALS data efficiently. An efficient set of conditions were defined to choose the training data semi-automatically when an expert user is not available. Four standard study sites were used to evaluate the performance of the method. The obtained results were compared with four popular filtering algorithms based on type I error, type II error, the kappa coefficient and the total error. First echoes were used in the proposed method to increase the reliable detection of vegetated areas. The proposed algorithm has an easy implementation procedure and low computational costs. The results obtained for both semiautomatic and supervised training data selection reveal acceptable accuracies, especially for type II errors. Use of this algorithm would lead to high-quality DTM generation using accurately identified bare-earth points in urban areas.