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With the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored by non-destructive techniques. The work outlines the approaches that were applied to the remotely sensed data to reveal potential remains: Visualization methods (e.g., hillshade and slope raster images), ISODATA clustering, and Support Vector Machine (SVM) algorithms. The results display various archaeological remains within the study site that have been successfully identified. Applying multiple methods and algorithms have successfully improved our understanding of spatial attributes within the landscape. The outcomes demonstrate how raster derivable from inexpensive approaches can be used to identify archaeological remains and hidden monuments, which have the possibility to revolutionize archaeological understanding.
Kadhim et al. (Tue,) studied this question.