摘要
构建了含有一个隐含层的两层BP神经网络反演模型,以TM数据的前4个波段的反射率作为输入,以悬浮物浓度值作为输出,成功反演了太湖水体的悬浮物浓度。
Abstract： A two-layer BP neural net model is constructed with four input nodes of TM1,2,3, 4 hand reflectances, and one output node of suspended solid concentration（SSC） to retrieve SSC of Lake Taihu. The results demonstrated that BP neural net is very fit to quantitatively retrieve water quality of case Ⅱ water with complex optic characteristic, and has much higher accuracy than the common linear model. A test was made and the results suggest that 13 had relative error （RE）RE of less than 30%, accounting for 81.25% of the total samples.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2006年第8期683-686,735,共5页
Geomatics and Information Science of Wuhan University
基金
中国科学院南京地理与湖泊研究所所长基金资助项目
关键词
BP神经网络模型
悬浮物浓度
太湖
定量反演
BP neural net
suspended solid concentration
Lake Taihu
quantitative retrieval
