@article{Tapinaki2023,

title = {CONVENTIONAL OR AUTOMATED PHOTOGRAMMETRY FOR CULTURAL HERITAGE DOCUMENTATION?},

author = {S. Tapinaki and M. Pateraki and M. Skamantzari and A. Georgopoulos},

url = {https://doi.org/10.5194/isprs-archives-XLVIII-M-2-2023-1535-2023},

doi = {10.5194/ISPRS-ARCHIVES-XLVIII-M-2-2023-1535-2023},

issn = {1682-1750},

year  = {2023},

date = {2023-06-26},

urldate = {2023-01-01},

journal = {The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},

volume = {XLVIII-M-2-2023},

pages = {1535-1542},

publisher = {Copernicus GmbH},

abstract = {Abstract. During the past 15 years photogrammetric practice has experienced an unprecedented change by the influence of computer vision algorithms, which support an almost completely automated processing. It is widely acknowledged that this fact has “democratized” Photogrammetry a lot, in the sense that it has become almost everyone’s tool. However, this radical change has been met by scepticism by traditional photogrammetrists, who claim that such tools may lead to geometrically wrong and inaccurate results if not accompanied by thorough projection and error checks and evaluation of the correctness of results.In this paper, the two approaches are briefly described on the basis of the geometric documentation of a cultural heritage funerary monument situated in the archaeological site of Messini in Southern Greece. An effort is made for highlighting the obvious advantages of each approach but also indicating their disadvantages. Applications, subject to different requirements and processing procedures are identified, rationalizing that conventional photogrammetric procedures still cannot be easily replaced.},

keywords = {Cultural heritage Documentation, MVS, Orthophoto, SfM, Stereoscopic photogrammetry},

pubstate = {published},

tppubtype = {article}

}

@article{Agrafiotis2020,

title = {Correcting Image Refraction: Towards Accurate Aerial Image-Based Bathymetry Mapping in Shallow Waters},

author = {Panagiotis Agrafiotis and Konstantinos Karantzalos and Andreas Georgopoulos and Dimitrios Skarlatos},

url = {https://doi.org/10.3390/rs12020322 https://www.mdpi.com/2072-4292/12/2/322},

doi = {10.3390/rs12020322},

issn = {2072-4292},

year  = {2020},

date = {2020-01-01},

journal = {Remote Sens.},

volume = {12},

number = {2},

pages = {322},

publisher = {MDPI AG},

abstract = {Although aerial image-based bathymetric mapping can provide, unlike acoustic or LiDAR (Light Detection and Ranging) sensors, both water depth and visual information, water refraction poses significant challenges for accurate depth estimation. In order to tackle this challenge, we propose an image correction methodology, which first exploits recent machine learning procedures that recover depth from image-based dense point clouds and then corrects refraction on the original imaging dataset. This way, the structure from motion (SfM) and multi-view stereo (MVS) processing pipelines are executed on a refraction-free set of aerial datasets, resulting in highly accurate bathymetric maps. Performed experiments and validation were based on datasets acquired during optimal sea state conditions and derived from four different test-sites characterized by excellent sea bottom visibility and textured seabed. Results demonstrated the high potential of our approach, both in terms of bathymetric accuracy, as well as texture and orthoimage quality.},

keywords = {Aerial imagery, Bathymetry, Coastal mapping, DSM, Image correction, Machine Learning, Refraction correction, Seabed Mapping, SfM, UAV},

pubstate = {published},

tppubtype = {article}

}