摘要
利用Landsat7 ETM+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,并对长江口南港河段水深进行了反演。结果表明:具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5 m的水深值反演精度较高,而对大于10 m的水深值反演精度较低。
A momentum BP neural network model （MBPNNM） was constructed to retrieve the water depth information for the South Channel of the Yangtze River Estuary using the relationship between reflectance derived from l_andsat ETM ＋ satellite data and water depth information. Results show that MBPNNM, which exhibits a strong capability of non-linear mapping, allows the water depth information in the study area to be retrieved at a relatively high accuracy level. Affected by the sediment concentration of water in the estuary, MBPNNM enables the retrieval of water depth of less than 5 meters accurately. However, the accuracy is not ideal for the water depth of more than 10 meters.
出处
《海洋工程》
CSCD
北大核心
2005年第4期33-38,共6页
The Ocean Engineering
基金
国家自然科学基金重点资助项目(50339010)
国家"十五"
"211"资助项目
关键词
长江口
BP神经网络
水深遥感
反演模型
Yangtze River Estuary
BP neural network
water-depth remote sensing
retrieval model
