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dc.contributor.authorGuirado Hernández, Emilio
dc.contributor.authorBlanco Sacristám, Javier
dc.contributor.authorRodríguez Caballero, Emilio
dc.contributor.authorTabik, Siham
dc.contributor.authorAlcaraz Segura, Domingo
dc.contributor.authorMartínez Valderrama, Jaime
dc.contributor.authorCabello García, Tomás
dc.date.accessioned2021-01-11T10:48:49Z
dc.date.available2021-01-11T10:48:49Z
dc.date.issued2021-01-05
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10835/9269
dc.description.abstractVegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectdeep-learninges_ES
dc.subjectfusiones_ES
dc.subjectmask R-CNNes_ES
dc.subjectobject-basedes_ES
dc.subjectoptical sensorses_ES
dc.subjectcattered vegetationes_ES
dc.subjectvery high-resolutiones_ES
dc.titleMask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensorses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/1/320es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional