Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

Autor(en): Storch, Marcel
de Lange, Norbert 
Jarmer, Thomas 
Waske, Bjorn 
Stichwörter: AIRBORNE; AUTOMATIC DETECTION; Cultural differences; Engineering; Engineering, Electrical & Electronic; EXTRACTION; Filtering algorithms; Geography, Physical; Historical terrain anomalies; Imaging Science & Photographic Technology; INTERPOLATION; LANDSCAPE; Laser radar; machine learning; ONE-CLASS CLASSIFICATION; Physical Geography; REGULARIZED SPLINE; Remote Sensing; SEGMENTATION; splines; Splines (mathematics); Support vector machines; SURFACE; TENSION; UAV-LiDAR; Vegetation mapping
Erscheinungsdatum: 2023
Herausgeber: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Journal: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volumen: 16
Startseite: 3158
Seitenende: 3173
Zusammenfassung: 
The documentation of historical remains and cultural heritage is of great importance to preserve historical knowledge. Many studies use low-resolution airplane-based laser scanning and manual interpretation for this purpose. In this study, a concept to automatically detect terrain anomalies in a historical conflict landscape using high-resolution UAV-LiDAR data was developed. We applied different ground filter algorithms and included a spline-based approximation step in order to improve the removal of low vegetation. Due to the absence of comprehensive labeled training data, a one-class support vector machine algorithm was used in an unsupervised manner in order to automatically detect the terrain anomalies. We applied our approach in a study site with different densities of low vegetation. The morphological ground filter was the most suitable when dense near-ground vegetation is present. However, with the use of the spline-based processing step, all filters used could be significantly improved in terms of the F1-score of the classification results. It increased by up to 42% points in the area with dense low vegetation and by up to 14% points in the area with sparse low vegetation. The completeness (recall) reached maximum values of 0.8 and 1.0, respectively, when taking into account the results leading to the highest F1-score for each filter. Therefore, our concept can support on-site field prospection.
ISSN: 1939-1404
DOI: 10.1109/JSTARS.2023.3259200

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