基于SegFormer的无人机遥感图像语义分割
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer
AI总结：
针对无人机遥感图像语义分割需求，评估了SegFormer框架从B0到B5各变体在UAVid数据集上的表现，验证了其兼具高效率与高精度的地物分割能力。
摘要：
无人机（UAV）作为遥感平台的应用日益广泛，已受到广泛关注，在地面目标识别方面极具价值。卫星遥感图像存在分辨率受限、易受天气影响等问题，而采用低速无人机的无人机遥感具备更高的目标分辨率与灵活性。先进机器学习技术的出现推动了图像分析领域的重大进展，尤其是无人机遥感图像的语义分割方向。本文评估了语义分割框架SegFormer在无人机图像语义分割任务中的效果与效率。研究采用专为语义分割任务构建的UAVid数据集，对SegFormer的多个变体（从实时型B0到高性能型B5）进行了测评，详细阐述了面向无人机语义分割场景的SegFormer架构与训练流程。实验结果展示了该模型在基准数据集上的性能，凸显其在多种无人机应用场景中精准勾勒目标与土地覆盖特征的能力，兼具高效率与高性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
