2018
DOI: 10.1016/j.culher.2017.12.009
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On the characterization of temporal and spatial patterns of archaeological crop-marks

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Cited by 28 publications
(26 citation statements)
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“…The high number of detected features characterized by possible archaeological interest were compared with ancillary data available from independent data sources as described in 1.1. Different tools were used for the validation (i) match with bibliography related to the investigated area [20][21][22][23][24][25][26][27][34][35][36][37]107,108]; (ii) comparison with Google Earth multi-temporal high-resolution images [109][110][111]; (iii) comparison with data of the National GeoPortal of the Italian Ministry of the Environment (http://www.pcn.minambiente.it/viewer/), and (iv) the data on the WebGis CartApulia (http://www.cartapulia.it/).…”
Section: Resultsmentioning
confidence: 99%
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“…The high number of detected features characterized by possible archaeological interest were compared with ancillary data available from independent data sources as described in 1.1. Different tools were used for the validation (i) match with bibliography related to the investigated area [20][21][22][23][24][25][26][27][34][35][36][37]107,108]; (ii) comparison with Google Earth multi-temporal high-resolution images [109][110][111]; (iii) comparison with data of the National GeoPortal of the Italian Ministry of the Environment (http://www.pcn.minambiente.it/viewer/), and (iv) the data on the WebGis CartApulia (http://www.cartapulia.it/).…”
Section: Resultsmentioning
confidence: 99%
“…Moreover, in order to identify the best period of the features visibility, TSA was coupled with the Spectral Unmixing analysis, carried out (using the ENVI 5.3 software). Of course, the best period/season in which the phenomenon of soil and crop marks are evident can be different from one index to another and from one features to another even in the same image due effect of local factors [21,51]. Spectral Un-mixing is used to show single pixels values in the images as: (i) minimum value; (ii) maximum value; (iii) average; (iv) standard deviation.…”
Section: Index Equation Referencementioning
confidence: 99%
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“…We have well published examples of hyperspectral imagery showing features invisible in panchromatic imagery (e.g. Bertok, Gati and Bela 2015;Doneus et al 2014;Masini et al 2018) and hyperspectral imagery is often collected at a lower resolution e.g. 1-2 m than aerial photography, with a typical resolution of 20-50 cm.…”
Section: Prioritizing Improving Data Fusionmentioning
confidence: 99%