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《Earth Science》 2017-12
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Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network

Zheng Guizhou;Le Xiaodong;Wang Hongping;Hua Weihua;Faculty of Information Engineering,China University of Geosciences;  
The inversion of water depth from remote sensing imagery is an important technology of depth measurement.In this paper,on the basis of radiometric calibration and atmospheric correction,BP(back propagation)and RBF(radial basis function)neural networks were built to retrieve water depth from WorldView-02 high-resolution satellite imagery in Mischief reef.Band1 to band 8 of satellite imagery were used as the input data of the neural networks.Then,they were converted from input layer to hidden layer and from the hidden layer to output layer with tansig,logsig,Gaussian and purelin functions.Finally,the accuracy of the two models was evaluated by R2(coefficient of determination),MAE(mean absolute error),RMSE(root mean square error)and the regression analysis between retrieved water depth and ground measured water depth.The results show that RBF neural network has simpler model structure,and lower requirement of samples.Besides,its retrieval accuracy reaches0.995.Therefore,RBF neural network is more suitable for the inversion of water depth.
【Fund】: 基金项目 巴拉望岛附近海域基础地质调查遥感解译
【CateGory Index】: P714;P715.7
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