利用高光谱数据估算太湖水体CDOM浓度的神经网络模型

Remote Sensing Retrieval of CDOM Concentration in Lake Taihu with Hyper-spectral Data and Neural Network Model

摘要: 利用2007-11-08~2007-11-21 14 d时间对太湖74个样点进行了水质取样分析和波谱实测。在分析水体固有光学特性的基础上,确定了CDOM浓度遥感反射比的敏感波段,建立了湖泊水体CDOM浓度反演的神经网络模型。结果表明,隐含层节点数为10的神经网络模型在各神经网络模型中效果最佳。利用验证样本对神经网络模型和其他算法模型进行误差分析,发现神经网络模型更适用于湖泊水体。

Abstract: CDOM concentration is an important parameter of water environment.In order to accurately retrieve CDOM concentration in lakes,a field experiment including water quality analysis and spectrum measurements was carried out on 74 stations of Lake Taihu during 14 days from Nov.8,2007 to Nov.21,2007.Based on the analysis of water inherent optical properties,sensitive wavebands were selected and neutral network models of CDOM concentration retrieval were established.The results show that a model with 10 nerve cells in hidden layer performs best,whose R is 0.887 and RMSE is 0.156.Meanwhile,the predicative errors of neutral network model and previous algorithm models were analyzed through validation samples.Average relative error of the former is(12.8±29.9)%,while others are very large,which indicates that neutral network model is more suitable for CDOM concentration retrieval in Lakes than other algorithm models.