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基于BP神经网络模型的太湖悬浮物浓度遥感定量提取研究
Quantitative Retrieval of Suspended Solid Concentration in Lake Taihu Based on BP Neural Net
【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 band 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.
【关键词】 BP神经网络模型；
悬浮物浓度；
太湖；
定量反演；
【Key words】 BP neural net； suspended solid concentration； Lake Taihu； quantitative retrieval；
【Key words】 BP neural net； suspended solid concentration； Lake Taihu； quantitative retrieval；
【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
- 【文献出处】 武汉大学学报(信息科学版) ,Geomatics and Information Science of Wuhan University , 编辑部邮箱 ,2006年08期
- 【分类号】TP79
- 【被引频次】45
- 【下载频次】473