Autodetection of ancient Arabian tombs in high-resolution satellite imagery

Schuetter, Jared; Goel, Prem K.; McCorriston, Joy; Park, Jihye; Senn, Matthew; Harrower, Michael J.

doi:10.1080/01431161.2013.802054

Cited by 62 publications

(17 citation statements)

References 28 publications

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“…Wonsok et al (2013) applied a semiautomatic method based on the local orientation using genetic algorithm for the detection of circular marks related to ancient graves in the desert of Xinjiang (China). Schuetter et al (2013) proposed a semiautomatic approach for the detection of circular features related to ancient Arabian tombs.…”

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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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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“…The human eye remains an adept feature extractor and can distinguish linear or circular structures and earthworks easily from the natural soil [6]. More recently, however, automatic approaches in pattern recognition have also become common, often based on computer algorithms adopted from other disciplines [7][8][9][10], and tested for archaeological purposes to detect color [11,12], changes in topography [13,14] or different reflection patterns [15].…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

et al. 2017

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Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km 2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.

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“…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%

“…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%

“…In the extraction phase, the previously trained algorithm detects archaeological traces in a new unlabeled set of images. 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.…”

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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Autodetection of ancient Arabian tombs in high-resolution satellite imagery

Cited by 62 publications

References 28 publications

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

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

Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

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

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