Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Abstract

There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.

Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy

“…RGB sensor were utilized. The thermoMap features a 0.3 megapixel sensor recording infrared wavelengths from 7500-13,500 nm, with a sensor resolution of 0.1 • C. The sensor performs a radiometric calibration automatically in-flight and collects images in timelapse mode at a rate of 7.5 images per second [48]. The S.O.D.A.…”

“…The S.O.D.A. camera has a 20 megapixel sensor, global shutter, and collects RGB imagery simultaneously on a single CMOS (complementary metal-oxide-semiconductor sensor) [48]. We used the senseFly eMotion flight planning and mission implementation software for all phases of planning and data collection (v. 3.5.0, senseFly, 2019).…”

“…Additionally, the use of commercially available GCPs referenced to very-high-accuracy spatial coordinates is common for visible RGB and multispectral imagery, but much less so for thermal imagery, mostly due to the fact that thermal cameras cannot "see" standard GCP targets as can other sensor types. Within the field of thermography, alternative "passive" GCPs, or GCPs made from materials with different emissivity than the surrounding environment, have been used, including aluminum targets [47][48][49][50][51], as well as gray tarp [52], black polypropelyne [48], and wood panels painted gray [53]. Less commonly utilized in thermal data collection with UAVs are "active" GCPs, or GCPs that are warmed or cooled to a temperature that is different than the background temperature.…”

Integrated results of aerial image, ground magnetics and excavation for settlement assessment at Dadan site, Al‐’Ula area, Saudi Arabia

“…The use of spaceborne remote sensing tools in archaeological work can be generally categorized into three types: multispectral, hyperspectral and synthetic aperture radar (SAR) (Abrams & Comer, 2013; Agapiou, Hadjimitsis, Sarris, Georgopoulos, & Alexakis, 2013; Aqdus, Hanson, & Drummond, 2012; Dore, Patruno, Pottier, & Crespi, 2013; Lasaponara, Leucci, Masini, Persico, & Scardozzi, 2016; Stewart, Lasaponara, & Schiavon, 2014; Tapete, 2019). During recent decades, many research results have emphasized the importance and success of various remote sensing techniques in archaeological applications (Brooke & Clutterbuck, 2020; Deng, Guo, Wang, & Nie, 2010; Lasaponara & Masini, 2012). The majority of the commonly known aerial imagery techniques based on commercial data resources are costly and time consuming (Cajigas, 2017).…”

Farklı Piksel Tabanlı Sınıflandırma Yöntemlerinin Arazi Kullanımı ve Arazi Örtüsü Belirlemedeki Performansının İncelenmesi

“…Kim and Ryu (2020) created a sedimentary surface map based on UAV and object-based image analysis (OBIA). Brooke and Clutterbuck (2020) have proposed a methodology for examining archaeological sites with UAVs that do not have obvious surface features. Louargant et al (2017) used the spectral information potential of images captured with UAV to differentiate crop-weed discrimination.…”