经验评估UNet用于对地震传感器安装适用表面进行细分
Mikhail Uzdiaev1, Marina Astapova1, Andrey Ronzhin1
1St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14th Line, St. Petersburg 19917, Russia.
Journal of imaging
|January 27, 2026
概括
这项研究评估了U-Net深度学习模型,用于使用卫星图像识别地震传感器位置. 具有RGB和近红外频段的CSPDarknet53为自动化地震网络规划提供了最佳性能.
科学领域:
- 地质物理学和远程传感
- 深度学习应用程序
- 地理空间分析是什么
背景情况:
- 无线地震节点系统需要最佳的传感器放置才能有效地获取数据.
- 确定合适的安装地点包括评估地面稳定性和尽量减少干扰.
- 卫星图像的语义细分为自动选址提供了一个潜在的解决方案,但它对地震网络的应用是新的.
研究的目的:
- 实证地评估U-Net架构用于适合地震传感器安装的表面的语义细分.
- 通过使用定制标记的Sentinel-2数据集,为这个新型应用程序建立基线性能.
- 调查不同预训练编码器,光谱带和输入修改对细分精度的影响.
主要方法:
- 利用了一套新的Sentinel-2多谱图像数据集,专门标记为地震传感器安装适用性.
- 使用U-Net卷积神经网络架构与各种预训练的编码器 (EfficientNetB2,CSPDarknet53,MAxViT).
- 用不同的Sentinel-2光谱带组合进行了实验,并修改了输入卷积层的步伐,以增强小物体细分.
主要成果:
- 跨阶段部分暗网53 (CSPDarknet53) 编码器表现出卓越的性能,实现了0.534的IOU,0.716的精度和0.635的回忆.
- 红色,绿色,蓝色 (RGB) 和近红外 (NIR) 频段的组合,分辨率为10米/像素,在各种配置中提供了最强大的结果.
- 将输入步幅从2降低到1显著改善了小线性特征的细分,例如道路.
结论:
- CSPDarknet53编码器和RGB+NIR Sentinel-2频段为地震传感器位置选择中的语义细分提供了强大的基线.
- 修改输入步幅是一种用于改进与安装规划相关的关键线性特征的检测的实用技术.
- 这项研究为优化深度学习模型的优化提供了有价值的见解,用于自动化地震节点网络部署.
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