Publication: Automatic detection of uprooted orchards based on orthophoto texture analysis
| dc.contributor.author | Ciriza Labiano, Raquel | |
| dc.contributor.author | Sola Torralba, Ion | |
| dc.contributor.author | Albizua, Lourdes | |
| dc.contributor.author | Álvarez Mozos, Jesús | |
| dc.contributor.author | González de Audícana Amenábar, María | |
| dc.contributor.department | Proyectos e Ingeniería Rural | es_ES |
| dc.contributor.department | Landa Ingeniaritza eta Proiektuak | eu |
| dc.date.accessioned | 2017-11-15T09:28:42Z | |
| dc.date.available | 2017-11-15T09:28:42Z | |
| dc.date.issued | 2017 | |
| dc.description.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. | en |
| dc.description.sponsorship | This study was funded by the Spanish National Institute for Agricultural and Food Research and Technology (INIA) through its training program for researchers. | en |
| dc.format.extent | 22 p. | |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.doi | 10.3390/rs9050492 | |
| dc.identifier.issn | 2072-4292 | |
| dc.identifier.uri | https://academica-e.unavarra.es/handle/2454/26175 | |
| dc.language.iso | eng | en |
| dc.publisher | MDPI | en |
| dc.relation.ispartof | Remote sensing, 2017, 9(5), 492 | en |
| dc.relation.publisherversion | https://dx.doi.org/10.3390/rs9050492 | |
| dc.rights | © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Orchard detection | en |
| dc.subject | Image analysis | en |
| dc.subject | Texture feature | en |
| dc.subject | GLCM | en |
| dc.subject | Wavelet transform | en |
| dc.subject | Discriminant analysis | en |
| dc.subject | Parcel level classification | en |
| dc.title | Automatic detection of uprooted orchards based on orthophoto texture analysis | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
| dc.type.version | info:eu-repo/semantics/publishedVersion | en |
| dspace.entity.type | Publication | |
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