DecHPoints: A New Tool for Improving LiDAR Data Filtering in Urban Areas
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ORIGINAL ARTICLE
DecHPoints: A New Tool for Improving LiDAR Data Filtering in Urban Areas Sandra Buján1 · Chester Andrew Sellers2 · Miguel Cordero1 · David Miranda1 Received: 25 June 2018 / Accepted: 4 December 2019 © Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2020
Abstract Identifying ground points from LiDAR data remains a challenge more than 2 decades after automatic filtering methods were first developed. The efficacy of filtering methods depends on both the physical characteristics of the environment and on the quality of the data used. Other limitations, affecting accessibility and usability, include the choice of filter and identification of optimal parameter values. To address these problems, the most recent filters have increased their level of complexity combining different strategies, so-called hybrid methods. In this study, two tools are proposed to improve the previous filters: a decimation tool for non-ground points and a densification process. Our main improvement is to combine these tools and a filter, in this case the Iterative Robust Interpolation Filter (IRI) (Kraus and Pfeifer in ISPRS J Photogramm Remote Sens 53(4):193–203. https://doi.org/10.1016/S0924-2716(98)00009-4, http://www.sciencedirect.com/science/artic le/pii/S0924271698000094, 1998), to (1) improve the filtering results in urban areas by removing buildings prior to filtering, which enables a downsizing of cells used for the selection of ground points and (2) to reduce the influence of parameters on the filtering accuracy. We used two LiDAR data sets: the reference data were acquired from the International Society of Photogrammetry and Remote Sensing (ISPRS) and the high density LiDAR data. In the first case, the results obtained are compared with those obtained in previous studies, using the metrics proposed by Sithole and Vosselman (ISPRS J Photogramm Remote Sens 59(1–2):85–101, https://doi.org/10.1016/j.isprsjprs.2004.05.004, http://www.sciencedirect.com/scien ce/article/pii/S0924271604000140, 2004). For urban samples, the proposed hybrid method provided better results than the IRI algorithm, yielding a Kappa coefficient of 91.5%. The proposed method is one of the most accurate filters that has been tested with the ISPRS data. Finally, the results obtained on the basis of the high density LiDAR data reinforced the previous results and showed the potential usefulness of the proposed hybrid method. Keywords LiDAR ground identification · Decimation and densification tools · Hybrid method Zusammenfassung DecHPoints: Ein neues Werkzeug zur Verbesserung der Filterung von LiDAR-Daten in bebauten Gebieten. Die Identifizierung von Bodenpunkten in LiDAR-Daten bleibt auch über zwei Jahrzehnte nach der Entwicklung von ersten Filtermethoden eine Herausforderung. Der Erfolg der Filterung hängt sowohl von den physikalischen Eigenschaften der Umgebung als auch von * Sandra Buján [email protected] Chester Andrew Sellers [email protected] Miguel Cordero miguel.cordero@
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