Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer
Journal
arXiv preprint arXiv:2410.01092
Date Issued
2024-10-01
Author(s)
Abstract
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, em ploying 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 segmenta tion. 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.
considerable attention, proving invaluable for ground object recognition. While satellite remote
sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, em ploying 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 segmenta tion. 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.
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