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Remote Sens. 2016, 8(3), 235; doi:10.3390/rs8030235

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Remote Sensing Laboratory, National Technical University of Athens, Heroon Polytechniou 9, Zographos 15780, Greece
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Academic Editors: Mutlu Ozdogan, Clement Atzberger and Prasad S. Thenkabail
Received: 10 December 2015 / Revised: 23 February 2016 / Accepted: 29 February 2016 / Published: 12 March 2016
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
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Abstract

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum. View Full-Text
Keywords: object-based image analysis; classification; precision viticulture; spectral signatures; features; crops object-based image analysis; classification; precision viticulture; spectral signatures; features; crops
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Karakizi, C.; Oikonomou, M.; Karantzalos, K. Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data. Remote Sens. 2016, 8, 235.

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