Aerial Remote Sensing for Precision Archaeology Using RGB-Multispectral Image Fusion of UAS Data

Abstract

Precision Archaeology leverages advanced technologies, such as unmanned aircraft systems (UAS), for documenting archaeological sites with high spatial resolution and accuracy. This paper presents a reproducible RGB-multispectral (MS) image-fusion workflow for Precision Archaeology, combining PPK-based georeferencing with quantitative assessment of product accuracy and spectral preservation. Within this framework, the repeatability of the results produced by the UAS data fusion method confirms its reliability and establishes it as a valuable documentation tool. Among the experimental applications conducted to date, this paper adds two more: the Sanctuary of Eukleia at Aigai and the funerary ensemble in the Philippi plain, where Aerial Remote Sensing was performed using a UAS equipped with a Post-Processed Kinematic (PPK)-Global Navigation Satellite System (GNSS) receiver. A ground-based GNSS receiver was used to measure control points (CPs) and the base point used to correct the coordinates of the UAS image acquisition centers using the PPK method. For both archaeological sites, RGB and MS stereoscopic images were acquired from flight altitudes of 60 and 100 m, respectively, achieving an overall theoretical solution accuracy of under 2 cm. Digital surface models (DSMs) were generated with spatial resolutions of approximately 2 cm for the RGB and about 14 cm for the MS images, along with orthophotomosaics with spatial resolutions of roughly 1 cm for RGB and 7 cm for MS images. In the final stage, image fusion of the RGB and MS orthophotomosaics was applied, improving the spatial resolution of the MS orthophotomosaics from 7 cm to approximately 1 cm, while simultaneously preserving nearly all the original spectral information in the new fused images. Spectral preservation was quantified via band-wise correlation between the original MS and fused images (≈0.99 average for the Philippi dataset; ≈0.85 average for Aigai, likely influenced by a ~45 min RGB-MS acquisition gap and corresponding shadow/illumination differences). These new images can be used for classification purposes, enabling the identification of different materials and the detection of archaeological feature pathology with optimal spatial resolution and accuracy.

Keywords

Precision archaeology / Aerial remote sensing / UAS / Ultra-high resolution / Multispectral imaging / Spectral information / DSM / Orthophotomosaic / Image fusion

Acknowledgments

Sincere thanks are extended to A. Kyriakou, Director of the University Excavation of the Aristotle University of Thessaloniki in Aigai, Greece, for granting permission to collect data at the archaeological site of the Sanctuary of Eukleia. Warm thanks are also extended to V. Poulioudi, Head of the Ephorate of Antiquities of Drama, and to M. Sofronidou, archaeologist of the Ephorate of Antiquities of Drama, for granting permission to collect data at the funerary ensemble.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No original images or raw data will be made available on the locations, as they concern archaeological sites.

Funding

This research received no external funding.

Declaration of Competing Interest

The author declares no conflicts of interest.

References

[1] Kaimaris D. Assessment of the Planimetric and Vertical Accuracy of UAS-LiDAR DSM in Archaeological Site. Geomatics 2025, 5, 61. DOI:10.3390/geomatics5040061

[2] Aasen H, Honkavaara E, Lucieer A, Zarco-Tejada PJ. Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens. 2018, 10, 1091. DOI:10.3390/rs10071091

[3] Sun Z, Wang X, Wang Z, Yang L, Xie Y, Huang Y. UAVs as remote sensing platforms in plant ecology: Review of applications and challenges. J. Plant Ecol. 2021, 14, 1003-1023. DOI:10.1093/jpe/rtab089

[4] Lu B, He Y. Optimal spatial resolution of Unmanned Aerial Vehicle (UAV)-acquired imagery for species classification in a heterogeneous grassland ecosystem. GIScience Remote Sens. 2017, 55, 205-220. DOI:10.1080/15481603.2017.1408930

[5] Kaimaris D. Aerial Remote Sensing Archaeology—A Short Review and Applications. Land 2024, 13, 997. DOI:10.3390/land13070997

[6] Klehm C, Williamson DM, Bement CL, Bethke B. Remote, Rugged Field Scenarios for Archaeology and the Field Sciences: Object Avoidance and 3D Flight Planning with sUAS Photogrammetry. Remote Sens. 2024, 16, 1418. DOI:10.3390/rs16081418

[7] Lee J, Sanjmyatav B, Kang IU, Go A, Diimaajav E, Baatar G, et al. UAV-Based Remote Sensing, GIS, and Its Application in Archaeology: A Case Study of the Xiongnu Elite Burial Complex at Gol Mod-II, Mongolia. In Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-M-2-2025, 30th CIPA Symposium Heritage Conservation from Bits: From Digital Documentation to Data-driven Heritage Conservation, Seoul, Republic of Korea, 25-29 August 2025.

[8] Adamopoulos E, Rinaudo F. UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art. Drones 2020, 4, 46. DOI:10.3390/drones4030046

[9] Macchiarella ND, Adkins KA. Application of UAS Remote Sensing for Cultural Heritage Preservation and Archaeology. In Proceedings of the 2022 Tularosa Basin Conference Considering 10,000 years of Human Presence within Southern New Mexico from Late Paleo to Late Historic, Ruidoso, NM, USA, 7-9 July 2022.

[10] Fallavollita P, Balsi M, Esposito S, Melis MG, Milanese M, Zappino L. UAS for Archaeology—New Perspectives on Aerial Documentation. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W2, 2013 UAV-g 2013, Rostock, Germany, 4-6 September 2013.

[11] Costantino D, Rossi G, Pepe M, Leserri M. Experiences of TLS, terrestrial and UAV photogrammetry in Cultural Heritage environment for restoration and maintenance purposes of Royal Racconigi castle, Italy. In Proceedings of the 2022 IMEKO TC-4 Metrology for Archaeology and Cultural Heritage, Rende, Italy, 19-21 October 2022.

[12] Bakirman T, Bayram B, Akpinar B, Karabulut MF, Bayrak OC, Yigitoglu A, et al. Implementation of ultra-light UAV systems for cultural heritage documentation. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVI-2/W1- 2022, 9th Workshop 3D-ARCH 3D Virtual Reconstruction and Visualization of Complex Architectures, Mantua, Italy, 2-4 March 2022.

[13] Vacatello F, Roccella S, Vannini A. UAVIMALS: The “Open” Remote Sensing System for Surface Archaeological Investigations. In Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-4/W7- 2023, FOSS4G (Free and Open Source Software for Geospatial) 2023-Academic Track, Prizren, Kosovo, 26 June-2 July 2023.

[14] Calantropio A, Chiabrando F, Auriemma R. Photogrammetric underwater and UAS surveys of archaeological sites: The case study of the Roman shipwreck of Torre Santa Sabina. In Proceedings of the XXIV ISPRS Congress, Nice, France, 6-11 June 2021.

[15] Mouget A, Lucet G. Photogrammetric Archaeological Survey with UAV. In Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5, 2014, ISPRS Technical Commission V Symposium, Riva del Garda, Italy, 23-25 June 2014.

[16] Özyesil O, Voroninski V, Basri R, Singer A. A Survey of Structure from Motion. Acta Numer. 2017, 26, 305-364. DOI:10.1017/S096249291700006X

[17] Abate N, Roubis D, Aggeli A, Sileo M, Amodio MA, Vitale V, et al. An Open-Source Machine Learning-Based Methodological Approach for Processing High-Resolution UAS LiDAR Data in Archaeological Contexts: A Case Study from Epirus, Greece. J. Archaeol. Method Theory 2025, 32, 38. DOI:10.1007/s10816-025-09706-8

[18] Rodríguez-Bulnes J, Benavides López AJ, Pellitero RP, Martín Civantos MJ, Collazo RJ. The documentation of archaeological heritage through aerial photogrammetry and UAS-based LiDAR: The case study of the Espique valley (La Peza, Granada, Spain). Disegnarecon 2022, 15, 121-130. DOI:10.20365/disegnarecon.29.2022.12

[19] Linck R, Stele A, Gericke T. Comparison of GPR results with UAV photogrammetry at a Roman villa rustica in Noricum. In Proceedings of the 15th International Conference on Archaeological Prospection, Kiel, Germany, 28 March-1 April 2023.

[20] Abate N, Frisetti A, Marazzi F, Masini N, Lasaponara R. Multitemporal-Multispectral UAS Surveys for Archaeological Research: The Case Study of San Vincenzo Al Volturno (Molise, Italy). Remote Sens. 2021, 13, 2719. DOI:10.3390/rs13142719

[21] Czajlik Z, Árvai M, Mészáros J, Nagy B, Rupnik L, Pásztor L. Cropmarks in Aerial Archaeology: New Lessons from an Old Story. Remote Sens. 2021, 13, 1126. DOI:10.3390/rs13061126

[22] Kaimaris D, Kandylas A. Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications. Heritage 2020, 3, 1046-1062. DOI:10.3390/heritage3040057

[23] Kaimaris D. Image Fusion Capability from Different Cameras for UAV in Cultural Heritage Applications. Drones Auton. Veh. 2024, 1, 10002. DOI:10.35534/dav.2023.10002

[24] Kaimaris D. Measurement Accuracy and Improvement of Thematic Information from Unmanned Aerial System Sensor Products in Cultural Heritage Applications. J. Imaging 2024, 10, 34. DOI:10.3390/jimaging10020034

[25] Kaimaris D, Kalyva D. Aerial Remote Sensing and Urban Planning Study of Ancient Hippodamian System. Urban Sci. 2025, 9, 183. DOI:10.3390/urbansci9060183

[26] Kyriakou A. The sanctuary of Eukleia at Vergina-Aegae:Aspects of the development of a cult place in the heart of the Mace-donian Kingdom. In Proceedings of the Ancient Macedonia: The Birth of Hellenism at the Origins of Europe, Università La Sapienza, Facoltà di Lettere e Filosofia Aula Odeion (Museo dell’Arte Classica), Roma, Italy, 14-15 December 2017.

[27] WingtraOne GEN II, Technical Specifications.Available online: https://wingtra.com/wp-content/uploads/Wingtra-Technical-Specifications.pdf (accessed on 19 November 2025).

[28] RedEdge-MX Integration Guide.Available online: https://support.micasense.com/hc/en-us/articles/360011389334-RedEdge-MX-Integration-Guide (accessed on 19 November 2025).

[29] HiPer SR. Available online: https://mytopcon.topconpositioning.com/support/products/hiper-sr (accessed on 19 November 2025).

[30] González-Audícana M, Saleta JL, Catalán GR, García R. Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1291-1299. DOI:10.1109/TGRS.2004.825593

[31] Choi J, Yu K, Kim Y. A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Trans. Geosci. Remote Sens. 2011, 49, 295-309. DOI:10.1109/TGRS.2010.2051674

[32] Yin H. A joint sparse and low-rank decomposition for pansharpening of multispectral images. IEEE Trans. Geosci. Remote Sens. 2017, 55, 3545-3557. DOI:10.1109/TGRS.2017.2675961

[33] Yang S, Zhang K, Wang M. Learning low-rank decomposition for pan-sharpening with spatial-spectral offsets. IEEE Trans. Neural Netw. Learn. Syst. 2018, 29, 3647-3657. DOI:10.1109/TNNLS.2017.2736011

[34] Xue J, Zhao Y, Liao W, Chan J-W. Nonlocal tensor sparse representation and low-rank regularization for hyperspectral image compressive sensing reconstruction. Remote Sens. 2019, 11, 193. DOI:10.3390/rs11020193

[35] Chavez PS, Sides SC, Anderson JA. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT Panchromatic. Photogramm. Eng. Remote Sens. 1991, 57, 265-303. Available online: https://home.cis.rit.edu/-cnspci/references/dip/fusion/chavez1991.pdf?utm_source=chatgpt.com (accessed on 19 November 2025).

[36] Tu TM, Su SC, Shyu HC, Huang PS. A new look at IHS-like image fusion methods. Inf. Fusion 2001, 2, 177-186. DOI:10.1016/S1566-2535(01)00036-7

[37] Zheng S, Shi WZ, Liu J, Tian J. Remote sensing image fusion using multiscale mapped LS-SVM. IEEE Trans. Geosci. Remote Sens. 2008, 46, 1313-1322. DOI:10.1109/TGRS.2007.912737

[38] Kahaki SMM, Jan Nordin M, Ashtari AH, Zahra SJ. Deformation invariant image matching based on dissimilarity of spatial features. Neurocomputing 2016, 175, 1009-1018. DOI:10.1016/j.neucom.2015.09.106

[39] Ranchin T, Aiazzi B, Alparone L, Baronti S, Wald L. Image fusion—The ARSIS concept and some successful implementation schemes. ISPRS J. Photogramm. Remote Sens. 2003, 58, 4-18. DOI:10.1016/S0924-2716(03)00013-3

[40] Wald L, Ranchin TM, Mangolini M. Fusion of satellite images of different spatial resolutions-Assessing the quality of resulting images. Photogramm. Eng. Remote Sens. 1997, 63, 691-699.

[41] Wang Z, Ziou D, Armenakis C, Li D, Li Q. A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1391-1402. DOI:10.1109/TGRS.2005.846874

[42] Kaimaris D. Photogrammetric Processing of Digital Images in the Service of Archaeological Research: The Localization of Ancient Via Egnatia from Amphipolis to Philippi. Ph.D. Thesis, Aristotle University of Thessaloniki, Thessaloniki, Greece, 2006.

[43] Moghimi A, Celik T, Mohammadzadeh A, Pirasteh S, Li J. Optimizing relative radiometric normalization: Minimizing residual distortions in multispectral bitemporal images using trust-region reflective and Laplacian pyramid fusion. IEEE Geosci. Remote Sens. Lett. 2025, 22, 5001305. DOI:10.1109/LGRS.2025.3562276