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Article

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

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Multidisciplinary Institute for Environment Studies “Ramon Margalef” University of Alicante, Edificio Nuevos Institutos, Carretera de San Vicente del Raspeig s/n San Vicente del Raspeig, 03690 Alicante, Spain
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Andalusian Center for Assessment and monitoring of global change (CAESCG), University of Almeria, 04120 Almeria, Spain
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College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus, Cornwall TR10 9EZ, UK
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Agronomy Department, University of Almeria, 04120 Almeria, Spain
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Centro de Investigación de Colecciones Científicas de la Universidad de Almería (CECOUAL), 04120 Almeria, Spain
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Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
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Department of Botany, Faculty of Science, University of Granada, 18071 Granada, Spain
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iEcolab, Inter-University Institute for Earth System Research, University of Granada, 18006 Granada, Spain
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Department of Biology and Geology, University of Almeria, 04120 Almeria, Spain
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(1), 320; https://doi.org/10.3390/s21010320
Received: 17 December 2020 / Revised: 29 December 2020 / Accepted: 1 January 2021 / Published: 5 January 2021
(This article belongs to the Special Issue Deep Learning Methods for Remote Sensing)
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. View Full-Text
Keywords: deep-learning; fusion; mask R-CNN; object-based; optical sensors; scattered vegetation; very high-resolution deep-learning; fusion; mask R-CNN; object-based; optical sensors; scattered vegetation; very high-resolution
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MDPI and ACS Style

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

AMA Style

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(1):320. https://doi.org/10.3390/s21010320

Chicago/Turabian Style

Guirado, Emilio, Javier Blanco-Sacristán, Emilio Rodríguez-Caballero, Siham Tabik, Domingo Alcaraz-Segura, Jaime Martínez-Valderrama, and Javier Cabello. 2021. "Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors" Sensors 21, no. 1: 320. https://doi.org/10.3390/s21010320

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