基于改进的Pix2Pix模型进行远程传感图像分析和预测,用于智能城市的水环境保护
Li Wang1, Wenhao Li1, Xiaoyi Wang2
1Beijing Laboratory for Intelligent Environmental Protection, School of Artificial Intelligence, Beijing Technology and Business University, Beijing, P.R. China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种改进的3D-GAN模型,用于使用遥感图像准确地预测智慧城市的水质. 该方法增强了时空分析,优于现有的有效水环境保护技术.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 智慧城市倡议越来越依赖于有效的水环境保护战略.
- 使用遥感数据进行水质预测,在准确性和训练复杂性方面面临挑战.
- 由于反向差异和天气条件,现有的方法与不完美的遥感图像作斗争.
研究的目的:
- 为智慧城市开发先进的水质遥感图像分析和预测方法.
- 为了克服当前预测模型中低准确度和难以训练的局限性.
- 用时空遥感数据改进对水环境的预测.
主要方法:
- 远程传感图像的时间序列的新预处理技术,包括像素替换,空间重量矩阵修复和线性插值.
- 一个改进的Pix2Pix (3D-GAN) 模型,包含一个卷积式门式循环单元,带有通道注意力和一个带有残余结构的U网.
- 敌对训练使用叠加的历史遥感图像进行增强的时空预测.
主要成果:
- 与现有的方法相比,拟议的3D-GAN方法在预测水环境方面表现优越.
- 包括结构相似性,峰值信号与噪声比率,共弦相似性和相互信息在内的定量指标显著更高.
- 该模型通过分析遥感数据的空间和时间维度,有效地预测水质.
结论:
- 开发的方法为智能城市的精确水质预测提供了强大的解决方案.
- 集成先进的深度学习技术解决了远程传感图像分析的关键挑战.
- 这种方法有助于更有效的智能水环境保护和管理.
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