Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors Guirado, Emilio Blanco Sacristán, Javier Rodríguez-Caballero, Emilio Tabik, Siham Alcaraz Segura, Domingo Martínez-Valderrama, Jaime Cabello, Javier Deep learning Fusion Mask R-CNN Object-Based Image Analysis (OBIA) Optical sensor Scattered vegetation Very high-resolution This research was funded by the European Research Council (ERC Grant agreement 647038 [BIODESERT]), the European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612, the RH2OARID (P18-RT-5130) and RESISTE (P18-RT-1927) funded by Consejeria de Economia, Conocimiento, Empresas y Universidad from the Junta de Andalucia, and by projects A-TIC-458-UGR18 and DETECTOR (A-RNM-256-UGR18), with the contribution of the European Union Funds for Regional Development. E.R-C was supported by the HIPATIA-UAL fellowship, founded by the University of Almeria. S.T. is supported by the Ramon y Cajal Program of the Spanish Government (RYC-201518136). Vegetation 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. 2021-03-03T10:47:56Z 2021-03-03T10:47:56Z 2021-01-05 info:eu-repo/semantics/article Guirado, E.; Blanco-Sacristán, J.; Rodríguez-Caballero, E.; Tabik, S.; Alcaraz-Segura, D.; Martínez-Valderrama, J.; Cabello, J. Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors. Sensors 2021, 21, 320. [https://doi.org/10.3390/s21010320] http://hdl.handle.net/10481/66810 10.3390/s21010320 eng info:eu-repo/grantAgreement/EC/H2020/647038 BIODESERT http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España MDPI