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dc.contributor.authorGuirado, Emilioes_ES
dc.contributor.authorTabik, Siham es_ES
dc.contributor.authorAlcaraz Segura, Domingo es_ES
dc.contributor.authorCabello, Javieres_ES
dc.contributor.authorHerrera Triguero, Francisco es_ES
dc.date.accessioned2018-01-23T10:41:15Z
dc.date.available2018-01-23T10:41:15Z
dc.date.issued2017-11-26
dc.identifier.citationGuirado, E.; et al. Deep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Study. Remote Sensing, 9(12): 1220 (2017). [http://hdl.handle.net/10481/49111]es_ES
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10481/49111
dc.description.abstractThere is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been traditionally performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image often cannot be extrapolated to a different image. Recently, deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in computer vision and are offering promising results in land cover mapping. This paper analyzes the potential of CNN-based methods for detection of plant species of conservation concern using free high-resolution Google Earth TM images and provides an objective comparison with the state-of-the-art OBIA-methods. We consider as case study the detection of Ziziphus lotus shrubs, which are protected as a priority habitat under the European Union Habitats Directive. Compared to the best performing OBIA-method, the best CNN-detector achieved up to 12% better precision, up to 30% better recall and up to 20% better balance between precision and recall. Besides, the knowledge that CNNs acquired in the first image can be re-utilized in other regions, which makes the detection process very fast. A natural conclusion of this work is that including CNN-models as classifiers, e.g., ResNet-classifier, could further improve OBIA methods. The provided methodology can be systematically reproduced for other species detection using our codes available through (https://github.com/EGuirado/CNN-remotesensing).en_EN
dc.description.sponsorshipSiham Tabik was supported by the Ramón y Cajal Programme (RYC-2015-18136).en_EN
dc.description.sponsorshipThe work was partially supported by the Spanish Ministry of Science and Technology under the projects: TIN2014-57251-P, CGL2014-61610-EXP, CGL2010-22314 and grant JC2015-00316, and ERDF and Andalusian Government under the projects: GLOCHARID, RNM-7033, P09-RNM-5048 and P11-TIC-7765.en
dc.description.sponsorshipThis research was also developed as part of project ECOPOTENTIAL, which received funding from the European Union Horizon 2020 Research and Innovation Programme under grant agreement No. 641762, and by the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612.en
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/641762es_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licensees_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es_ES
dc.subjectZiziphus lotusen_EN
dc.subjectPlant species detectionen_EN
dc.subjectLand cover mappingen_EN
dc.subjectConvolutional Neural Networks (CNNs)en_EN
dc.subjectObject-Based Image Analysis (OBIA)en_EN
dc.subjectRemote sensing en_EN
dc.titleDeep-learning Versus OBIA for Scattered Shrub Detection with Google Earth Imagery: Ziziphus lotus as Case Studyen_EN
dc.typejournal articleen_EN
dc.rights.accessRightsopen accessen_EN
dc.identifier.doi10.3390/rs9121220


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