Potential of Full Waveform Airborne Laser Scanning Data for Urban Area Classification – Transfer of Classification Approaches Between Missions

Abstract

ABSTRACT:Full-waveform (FWF) LiDAR (Light Detection and Ranging) systems have their advantage in recording the entire backscattered signal of each emitted laser pulse compared to conventional airborne discrete-return laser scanner systems. The FWF systems can provide point clouds which contain extra attributes like amplitude and echo width, etc. In this study, a FWF data collected in 2010 for Eisenstadt, a city in the eastern part of Austria was used to classify four main classes: buildings, trees, waterbody and ground by employing a decision tree. Point density, echo ratio, echo width, normalised digital surface model and point cloud roughness are the main inputs for classification. The accuracy of the final results, correctness and completeness measures, were assessed by comparison of the classified output to a knowledge-based labelling of the points. Completeness and correctness between 90% and 97% was reached, depending on the class. While such results and methods were presented before, we are investigating additionally the transferability of the classification method (features, thresholds …) to another urban FWF lidar point cloud. Our conclusions are that from the features used, only echo width requires new thresholds. A data-driven adaptation of thresholds is suggested.

Full-Waveform Airborne Laser Scanning in Vegetation Studies—A Review of Point Cloud and Waveform Features for Tree Species Classification

“…Most pre-filtering steps serve to exclude noisy data or to detect structure related characteristics. Commonly applied filters are the minimum height (H min ) threshold and the number and index of return [13,27,[52][53][54][55] as well as the features echo width [13,[56][57][58][59][60][61][62][63][64][65], backscatter cross-section and backscatter coefficient [13,56,58,64,65].…”

“…Values have to be considered carefully, since the width depends on the strength of the received pulse [60,65]. To tackle this problem, [60] and [63] apply a normalization procedure in an urban environment, receiving an overall classification accuracy (>93%) and correctness (93%) for trees. Lin [60] uses the concept of Fuzzy Small membership functions in a defined neighborhood and [63] normalize the echo width by its minimum EW and maximum EW, which is found by using quantiles of the distribution of echo width.…”

“…To tackle this problem, [60] and [63] apply a normalization procedure in an urban environment, receiving an overall classification accuracy (>93%) and correctness (93%) for trees. Lin [60] uses the concept of Fuzzy Small membership functions in a defined neighborhood and [63] normalize the echo width by its minimum EW and maximum EW, which is found by using quantiles of the distribution of echo width. Such approaches improve the transfer of thresholds of normalized echo width from region A to region B.…”

Classificação De Nuvem De Pontos Laser Utilizando O Conceito De Análise De Componentes Principais E O Fator De Não Ambiguidade

“…Essas técnicas também têm sido utilizadas na classificação dos novos conjuntos de dados LASER: multiespectral (Wichmann et al, 2015) e full-waveform (Tran et al, 2015).…”

Automatic Airborne Laser Scanning Data Quality Control Procedure for Environmental Studies

“…We can verify the classification accuracy in many ways, but the main problem is the availability of reference data. The referenced data could be the manually classified point cloud data (Kim and Sohn 2010), alternative classification methods (Wężyk 2014), orthophoto (Yan et al 2012), aerial images (Antonarakis et al 2008), 2D vector databases (Matikainen et al 2009) or manually digitised vector layers (Tran et al 2015). The most common metrics to describe classification accuracy are the overall accuracy (OA) and Kappa (Guo et al 2015).…”