The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. The advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remote sensing images. This paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. The research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model’s performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance.
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer
Communic.Comp.Inf.Science
International Conference on Intelligent Systems and Pattern Recognition ; 2024 ; Istanbul, Türkiye June 12, 2024 - June 14, 2024
2025-03-05
15 pages
Article/Chapter (Book)
Electronic Resource
English
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer
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