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/article/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/science/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.
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 der Qualität der vorliegenden Daten ab. Weitere Randbedingungen, die die Anwendbarkeit beeinflussen, sind die Wahl des Filters und seine optimale Parametrisierung. Um einer Lösung der Probleme näher zu kommen, sind heutige Filter aufwändiger konstruiert und nutzen dabei die Kombination unterschiedlicher Strategien, wenden also so genannte Hybridverfahren an. In dieser Studie werden zwei Verfahren zur Verbesserung bisheriger Filter vorgeschlagen: Eine Methode zur Ausdünnung zwecks Eliminierung von Nicht-Bodenpunkten und ein Verfahren zur anschließenden Verdichtung der ausgedünnten Punktwolke. Unsere wichtigste Neuerung ist die Kombination dieser Werkzeuge verbunden mit einem Filter, in unserem Fall auf Basis der iterativen robusten Interpolation (IRI) (Kraus und Pfeifer, 1998). Einerseits soll dadurch eine Verbesserung der Filter-Ergebnisse in bebauten Gebieten durch Eliminierung von Gebäuden vor der Filterung erzielt werden, die eine Verkleinerung der Zellen für die Auswahl der Bodenpunkte ermöglicht, andererseits wird dadurch der Einfluss der Parameterauswahl auf die Genauigkeit der Filterung vermindert. Zur Evaluierung wurden zwei LiDAR-Datensätze verwendet: Referenzdaten der Internationalen Gesellschaft für Photogrammetrie und Fernerkundung (ISPRS) und sehr dichte LiDAR-Daten für ein Testgebiet aus Spanien. Beim ersten Datensatz wurden die Ergebnisse mit jenen aus früheren Studien unter Verwendung der von den Organisatoren des Tests (Sithole und Vosselman, 2004) vorgeschlagenen Metriken verglichen. Bei den städtischen Testgebieten lieferte die vorgeschlagene Hybridmethode mit einem Kappa-Koeffizient von 91,5% bessere Ergebnisse als der IRI-Algorithmus. Die Genauigkeitsmaße der vorgeschlagenen Methode liegen unter den besten, die anhand der ISPRS-Daten erzielt wurden. Die Ergebnisse für den zweiten Datensatz bestätigen die bereits genannten Ergebnisse und zeigen die potenzielle Nützlichkeit der vorgeschlagenen hybriden Filtermethode.
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Notes
A DTM is simply a statistical representation of the continuous surface of the ground by a large number of selected points with known X, Y, and Z coordinates in an arbitrary coordinate field.
Res is the size of the square cell that contains np points. It is obtained as follows: \(\textit{Res}=\sqrt{np/D}\), where D is the weighted mean of point density. np is the mean number of points per cell considered necessary to calculate the penetrability.
\(Sl=z_{\mathrm{max}}-z_{\mathrm{min}}/\sqrt{(x_{\mathrm{max}} -x_{\mathrm{min}})^2 +(y_{\mathrm{max}}-y_{\mathrm{min}})^2}\).
Xunta de Galicia (2009-PG239).
Civil UAVs Initiative Program, financed by Xunta de Galicia and developed by Babcock Mission Critical Services España S.A.U. and the Land Laboratory.
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Acknowledgements
We are indebted to our colleagues at LaboraTe for their valuable and constructive suggestions. We are responsible for any remaining error. We thank the anonymous referees, associate editor and the editor of the Journal of Photogrammetry, Remote Sensing and Geoinformation Science, whose comments helped us to improve this article. It is not hard to accept criticism if they allow you to enhance your work.
Funding
This study was supported by the Land Laboratory Research Group (G.I.-1934-TB) (Universidade de Santiago de Compostela, Spain) and the University of Azuay (Cuenca, Ecuador) (Project No: 2016-53).
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Buján, S., Sellers, C.A., Cordero, M. et al. DecHPoints: A New Tool for Improving LiDAR Data Filtering in Urban Areas. PFG 88, 239–255 (2020). https://doi.org/10.1007/s41064-019-00088-7
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DOI: https://doi.org/10.1007/s41064-019-00088-7