使用无人机图像和卫星图像的交叉视图地理定位算法
Jiqi Fan1, Enhui Zheng1, Yufei He1
1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
这项研究引入了一种新的单流金字塔变压器 (SSPT) 网络,用于无人机 (UAV) 交叉视图地理位置. SSPT模型显著提高了定位准确性,并在各种环境条件下表现出强大的性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 无人机 (UAV) 的交叉视图地理定位面临着由于不同图像来源和场景相似性而面临的挑战.
- 现有的方法难以应对干扰,需要强大的解决方案,以在卫星图像中准确定位无人机.
研究的目的:
- 开发一个先进的深度学习模型,以提高无人机交叉视图地理位置的准确性和稳定性.
- 通过利用注意力机制和专门的后处理技术来解决当前方法的局限性.
主要方法:
- 设计了一个单流金字塔变压器 (SSPT) 网络,包含自我注意和交叉注意机制.
- 实现了用于升级样本的标题模块和高斯权重窗口,以改善模型的融合.
- 利用风格转移技术来模拟环境变化以进行数据增强.
主要成果:
- 在UL14数据集上,SSPT-384模型获得了84.40%的相对距离得分 (RDS),这是一个显著的改进.
- 在3米,5米和20米的仪表水平精度 (MA) 提高了12%,12%和10%,分别与SSPT-384.4.
- SSPT-256型号也显示了性能增长,在各种值中改进了RDS和MA.
- 在包括热红外,夜间和雨天条件在内的扩展数据集上表现出强大的稳定性.
结论:
- 拟议的SSPT网络通过减轻干扰和改进特征相互作用,有效地提高无人机交叉视图地理位置的准确性.
- 该方法表现出极好的环境适应性和稳定性,使其适合于现实世界的应用.
- 结果验证了基于注意力的变压器架构和专门用于此任务的后处理的有效性.
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