摘要
利用遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,对缺失的1995年的水下地形资料进行了插补,并利用插补后的1973～2003年南港水下地形资料计算了南港河段的泥沙冲淤量,分析了南港河段等深线和横断面的变化,结果表明BP人工神经网络水深反演模型能反演出研究区的水深分布情况,特别是对水深浅于-5 m区域,模型反演效果更好.
A BP neural network model （BPNNM） was constructed to retrieve the water depth information of 1995 for the South Channel of the Yangtze River Estuary using the relationship between reflectance derived from satellite data and water depth information. The amount of deposition and erosion was calculated and the change of isobaths and cross section were analyzed using the under water topographical data from 1973 to 2003. Results show that the BPNNM allows the water depth information in the study area to be retrieved at a relatively high accuracy level specially for the water depth of less than 5 meters.
出处
《港工技术》
北大核心
2005年第3期4-6,共3页
Port Engineering Technology
基金
国家自然科学基金重点项目(50339010)
国家"十五""211"资助项目
关键词
长江口
GIS
RS
冲淤变化
可视化
the Yangtze Estuary,GIS
RS
change in the deposition
erosion visualization
