用SegFormer提升无人机遥感图像语义分割精度与效率
Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer
- 采用SegFormer架构，结合轻量级到高性能模型进行分割
- 在UAVid数据集上实现高精度物体与地表特征识别
- 适合需要快速高效分割的无人机遥感应用
无人机（UAV）作为遥感平台的应用日益广泛，相较于卫星遥感图像存在分辨率低、易受天气影响等局限，低速无人机可提供更高分辨率与更强灵活性。随着机器学习技术的发展，针对无人机遥感图像的语义分割取得显著进展。本文评估了SegFormer框架在无人机遥感图像语义分割中的有效性与效率，使用专为该任务设计的UAVid数据集，测试了从实时型（B0）到高性能型（B5）的多种模型变体。实验结果表明，该方法在基准数据集上表现优异，能准确分割各类地物与覆盖特征，在不同无人机场景下兼具高精度与高效率。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。