ArticleSensors (Basel, Switzerland)2021
Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Enhanced Mask R-CNN Incorporating CBAM and Soft-NMS for Identification and Monitoring of Offshore Aquaculture Areas.Sensors (Basel, Switzerland) · 2025Article
- An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region.Computational intelligence and neuroscience · 2022Article
- Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification.Sensors (Basel, Switzerland) · 2021Article
- Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
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
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