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在远程传感图像中有效检测云面具

Yichen Cui¹, Hong Shen², Chan-Tong Lam¹

¹Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR, China.

Sensors (Basel, Switzerland)

|December 17, 2024

概括

本研究介绍了MDU-Net,这是一种用于精确检测云和云影的新型深度学习模型. MDU-Net显著改善了小云目标的识别,并改进了边缘检测,以更好地分析天气和灾害.

科学领域:

遥感遥感遥感遥感
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

准确的云面罩检测对于天气和自然灾害研究至关重要.
由于不可预测的模式,现有的深度学习模型在精细的边缘提取和检测小云目标方面扎.

研究的目的:

开发一个先进的深度学习模型,以改善云和云影细分.
为了增强细云边缘和小云层的检测.

主要方法:

提出了MDU-Net,一个具有编码器-解码器-解码器架构的多尺度双上采样细分网络.
使用改进的剩余模块进行多级特征提取,并采用双重提取样品策略进行边缘精细化和上下文信息融合.

主要成果:

在基于Landsat8的云和云影数据集上,MDU-Net实现了95.61%的PA和84.97%的MIOU.
在细分精度和可视化方面表现优于现有模型.
在landcover.ai数据集上展示了强大的概括能力.

结论:

与其他方法相比,MDU-Net在云和云影探测方面提供了卓越的性能.
该模型有效地解决了边缘精细化和小目标检测方面的局限性.
MDU-Net显示出在天气监测和灾害管理方面的实际应用潜力.

关键词:

这是一个U形结构.云面罩检测云面罩检测背景信息完整的流量信息双增取样模块是一个双增取样模块.

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