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Remote Sens. 2017, 9(5), 492; doi:10.3390/rs9050492

Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis

1
Department of Projects and Rural Engineering, Campus Arrosadía, Public University of Navarre, Pamplona 31006, Spain
2
Tracasa, 31621 Sarriguren, Navarre, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Guoqing Zhou and Prasad S. Thenkabail
Received: 14 March 2017 / Revised: 10 May 2017 / Accepted: 13 May 2017 / Published: 17 May 2017
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Abstract

Permanent crops, such as olive groves, vineyards and fruit trees, are important in European agriculture because of their spatial and economic relevance. Agricultural geographical databases (AGDBs) are commonly used by public bodies to gain knowledge of the extension covered by these crops and to manage related agricultural subsidies and inspections. However, the updating of these databases is mostly based on photointerpretation, and thus keeping this information up-to-date is very costly in terms of time and money. This paper describes a methodology for automatic detection of uprooted orchards (parcels where fruit trees have been eliminated) based on the textural classification of orthophotos with a spatial resolution of 0.25 m. The textural features used for this classification were derived from the grey level co-occurrence matrix (GLCM) and wavelet transform, and were selected through principal components (PCA) and separability analyses. Next, a Discriminant Analysis classification algorithm was used to detect uprooted orchards. Entropy, contrast and correlation were found to be the most informative textural features obtained from the co-occurrence matrix. The minimum and standard deviation in plane 3 were the selected features based on wavelet transform. The classification based on these features achieved a true positive rate (TPR) of over 80% and an accuracy (A) of over 88%. As a result, this methodology enabled reducing the number of fields to photointerpret by 60–85%, depending on the membership threshold value selected. The proposed approach could be easily adopted by different stakeholders and could increase significantly the efficiency of agricultural database updating tasks. View Full-Text
Keywords: orchard detection; image analysis; texture feature; GLCM; wavelet transform; discriminant analysis; parcel level classification orchard detection; image analysis; texture feature; GLCM; wavelet transform; discriminant analysis; parcel level classification
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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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MDPI and ACS Style

Ciriza, R.; Sola, I.; Albizua, L.; Álvarez-Mozos, J.; González-Audícana, M. Automatic Detection of Uprooted Orchards Based on Orthophoto Texture Analysis. Remote Sens. 2017, 9, 492.

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