基于改进的YOLOv5ds的高分辨率遥感图像的建筑细分和提取方法
Fangzhe Chang1,2, Tianyue Ma2, Dantong Wang1
1Hebei Provincial Communication Planning, Design, and Research Institute Co., Ltd Town Renewal Research Center, Shijiazhuang, China.
PloS one
|March 18, 2025
概括
一种名为YOLOv5ds-RC的新方法通过细化轮来改善遥感图像中的建筑细分. 这种方法提高了土地利用监测和历史变化分析的准确性和速度.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 地理空间分析的研究.
背景情况:
- 遥感图像分析在建筑物细分方面面临挑战,包括复杂的城市景观和轮提取困难.
- 现有的方法在准确性和速度方面扎,阻碍了诸如土地利用监测等应用.
- 来自Gaofen-2等卫星的高分辨率图像具有潜力,但需要先进的处理技术.
研究的目的:
- 开发和评估一种用于高分辨率遥感图像构建细分和提取的新方法.
- 解决现有的建筑检测算法在轮精度和提取速度方面的局限性.
- 提高建筑提取的效率和客观性,用于诸如土地用途变化分析等应用.
主要方法:
- 提出了YOLOv5ds-RC,这是一种使用YOLOv5ds网络集成目标检测,语义细分和边缘优化的方法.
- 从特征金字塔网络 (FPN) 实现了一个上采样和卷积层分支,用于详细的语义细分.
- 包含一个Raster压缩模块,用于改进细分轮和纠正非直角图像扭曲.
主要成果:
- 与原始YOLOv5ds相比,YOLOv5ds-RC实现了更高的性能,精度从0.81483增加到0.8849.
- 回忆力从0.51332提高到0.63904,平均精度 (mAP) 从0.34922提高到0.47388.
- 该方法证明了有效的轮优化和精确地提取单个建筑物的规模,纠正目标位移偏差.
结论:
- YOLOv5ds-RC显著提高了高分辨率遥感图像中的建筑细分和提取精度和速度.
- 精细的轮提取能力对于详细的城市分析和历史变化检测至关重要.
- 这种方法为完全自动化快速提取提供了强大的解决方案,推进了土地利用变化监测.
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