Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network

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. Band 1 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 reaches 0.995. Therefore, RBF neural network is more suitable for the inversion of water depth.