Semi-automatic detection of linear archaeological traces from orthorectified aerial images

Figorito, Benedetto; Tarantino, Eufemia

doi:10.1016/j.jag.2013.04.005

Cited by 30 publications

(18 citation statements)

References 25 publications

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“…Multispectral information from satellite images was a fundamental approach employed in the detection of buried negative structures (drainage channels and circular crop marks), which were invisible in situ because of the flatness of the terrain and the impact of different land‐use strategies. With the passage of time and the development of on‐board systems, many studies worldwide have used aerial or satellite information and DTMs: De Laet, Paulissen, and Waelkens () used the Ikonos system to identify archaeological structures in Turkey; Lasaponara and Masini () used Aster and QuickBird images in Tiwanaku to locate several structures, including buried channels; Trier, Larsen, and Solberg () detected circular structures in Norway from high‐resolution satellite images; Menze and Ur () have mapped patterns of settlements in Northern Mesopotamia; Figorito and Tarantino () are conducting research in Italy for the detection of archaeological signs using high‐resolution aerial images, and Lasaponara, Leucci, Masini, Persico, and Scardozzi () explored the use of remote sensing in Hierapolis, Turkey. However, even the highly accurate QuickBird images cannot correctly detect minor yet important structures, such as domestic buildings.…”

Section: Introductionmentioning

confidence: 99%

A new environmental and spatial approach to the Tiwanaku World Heritage site (Bolivia) using remote sensing (UAV and satellite images)

González

Revilla

2019

Geoarchaeology

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This paper analyses the research carried out in the Tiwanaku World Heritage site in Bolivia, using unmanned aerial vehicles and satellite images. The combined use of images with different scales has made it possible to locate many archaeological structures unknown to date (drainage systems, walls, circular crop marks, and a possible dock). The Sentinel‐2 images, which were processed using principal components analysis and histogram equalization, show the river beds, flood‐prone areas, and several buried drainage channels surrounding the most important structures. The archaeological evidence obtained with the digital terrain model and natural color/multispectral images enables us to contrast a new dimension of land and water uses that go beyond what was known to date. In the same way, these images enable us to understand in detail the environmental characteristics, land use, building distribution, and flood defense structures of the Tiwanaku culture throughout its history, within the context of the environmental conditions of the Bolivian altiplano. This study allowed the authors to collect new information and pose questions on the relationship of this site with water, as well as a better understanding of the extent and habitat of this historical population.

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Section: Introductionmentioning

confidence: 99%

A new environmental and spatial approach to the Tiwanaku World Heritage site (Bolivia) using remote sensing (UAV and satellite images)

González

Revilla

2019

Geoarchaeology

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“…Tarantino and Figorito (2014) and Figorito and Tarantino (2014) applied a supervised classification, and therefore a semi-automatic procedure, based on segmentation of historical aerial photographs.…”

Section: Introductionmentioning

confidence: 99%

Towards an operative use of remote sensing for exploring the past using satellite data: The case study of Hierapolis (Turkey)

Lasaponara

Leucci

Masini

et al. 2016

Remote Sensing of Environment

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This paper is focused on the presentation and discussion of an object oriented approach, applied to the Hierapolis (Turkey) site, to automatically detect the subtle features linked to buried archaeological remains. The data processing is applied twice: (i) first, globally at the whole image and, (ii) second, at the significant subsets identified by global analysis, in order to refine the previously obtained categorization. Object oriented approaches are usually based on two main steps: i) first the segmentation, ii) then the classification. Herein, we first performed the unsupervised classification step and, then, the segmentation. This choice is given by the specificity of archaeological issue, in particular: (i) the subtle features/targets to be identified are partially or totally unknown and characterized by a very small spectral separability from the background, and therefore (ii) the discrimination between archaeological class and substrates likely suffers significant confusion. To cope with these issues, the first step is based on an unsupervised classification, which provides a first 'rough' categorization of pixels; the second step, based on the segmentation, enables us to extract the geometric shape, and, in turn, to only categorize as archaeological class those pixels belonging to geometrically (rectangular and linear) shaped clusters. Outputs from this classification identify rectangular and linear features of archaeological interest whose size suggested that they may be a farm and some sectors of an aqueduct, respectively. Results from satellite based analysis were successfully evaluated by georadar and geomagnetic prospection along with field survey. From georadar and geomagnetic prospection we were able i) to confirm the presence of buried remains and ii) to detail and characterize these archaeological features at the subsoil level as well as to define the local stratigraphy. From field survey we dated the detected buried remains to a period spanning from Imperial Roman to early Byzantine historical times.

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“…In the literature, most methods focus on applying radiometric enhancement, spatial filters and spectral indexes for archaeological investigations [15,[36][37][38]. In order to save time and manpower, several studies have tried to employ (semi-) automatic methods to extract archaeological traces [14,16,21,26,[39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

“…The supervised methods [34,[41][42][43][44] use machine learning concepts to train an algorithm to extract archaeological traces. In the learning phase, a training set of images containing archaeological traces labeled by domain experts is fed to an algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…A continuously-scalable template model technique was used to achieve extraction of the ancient Arabian tombs and agricultural crop marks in [41,42], respectively. D'Orazio et al [34] proposed a semi-automatic approach for archaeological trace extraction that used a region based active contour model; and in [43] the archaeological trace extraction approaches consisted of a combination of the active contour model, image segmentation, and object filtering analysis. In [44], unsupervised genetic algorithms and supervised support vector machines were tested together, which improved the extraction accuracy on ground-penetrating radar imagery.…”

Section: Introductionmentioning

confidence: 99%

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Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

et al. 2014

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Qanats in northern Xinjiang of China provide valuable information for agriculturists and anthropologists who seek fundamental understanding of the distribution of qanat water supply systems with regard to water resource utilization, the development of oasis agriculture, and eventually climate change. Only the tops of qanat shafts (TQSs), indicating the course of the qanats, can be observed from space, and their circular archaeological traces can also be seen in very high resolution imagery in Google Earth. The small size of the TQSs, vast search regions, and degraded features make manually extracting them from remote sensing images difficult and costly. This paper proposes an automated TQS extraction method that adopts mathematical morphological processing methods before an edge detecting module is used in the circular Hough transform approach. The accuracy OPEN ACCESSRemote Sens. 2014, 6 11957 assessment criteria for the proposed method include: (i) extraction percentage (E) = 95.9%, branch factor (B) = 0 and quality percentage (Q) = 95.9% in Site 1; and (ii) extraction percentage (E) = 83.4%, branch factor (B) = 0.058 and quality percentage (Q) = 79.5% in Site 2. Compared with the standard circular Hough transform, the quality percentages (Q) of our proposed method were improved to 95.9% and 79.5% from 86.3% and 65.8% in test sites 1 and 2, respectively. The results demonstrate that wide-area discovery and mapping can be performed much more effectively based on our proposed method.

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Semi-automatic detection of linear archaeological traces from orthorectified aerial images

Cited by 30 publications

References 25 publications

A new environmental and spatial approach to the Tiwanaku World Heritage site (Bolivia) using remote sensing (UAV and satellite images)

A new environmental and spatial approach to the Tiwanaku World Heritage site (Bolivia) using remote sensing (UAV and satellite images)

Towards an operative use of remote sensing for exploring the past using satellite data: The case study of Hierapolis (Turkey)

Automated Extraction of the Archaeological Tops of Qanat Shafts from VHR Imagery in Google Earth

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