Inversion of Water Depth from WorldView-02 Satellite Imagery Based on BP and RBF Neural Network
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摘要: 遥感水深反演是水深测量的一种重要技术和手段.以美济礁水深反演为例，选择WorldView-02高分影像为数据源，在辐射定标和大气校正的基础上，构建BP（Back Propagation）和RBF（Radial Basis Function）人工神经网络水深反演模型，以遥感影像8个波段为输入层，通过tansig、logsig、高斯函数和purelin函数变换实现从输入层到隐含层、隐含层到输出层的转换，以便反演水深.最后对反演水深与实测水深采用回归分析，求解决定系数（coefficient of determination，R2）、平均决定误差（Mean Absolute Error，MAE）、均方根误差（Root Mean Square Error，RMSE）等进行比较，评价2种模型的精度.结果表明，RBF神经网络模型结构更简单，对样本要求更低，反演精度达到0.995，更适合遥感水深反演.
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关键词:
- 遥感 /
- WorldView-02 /
- 水深反演 /
- BP神经网络 /
- RBF神经网络
Abstract: 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.-
Key words:
- remote sensing /
- WorldView-02 /
- water depth inversion /
- BP neural network /
- RBF neural network
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图 5 BP网络训练回归图
a.16, tansig, logsig；b.17, tansig, purelin；c.17, tansig, logsig；d, 17, logsig, logsig；e.18, tansig, logsig
Fig. 5. BP training network regression figure
表 1 辐射定标参数
Table 1. Radiometric calibration parameters
Band absCalFactorBand ΔλBand Coastal 9.295 654×10-3 4.730 000×10-2 Blue 1.783 568×10-2 5.430 000×10-2 Green 1.364 197×10-2 6.300 000×10-2 Yellow 6.810 718×10-3 3.740 000×10-2 Red 1.851 735×10-2 5.740 000×10-2 Red Edge 6.063 145×10-3 3.930 000×10-2 NIR-1 2.050 828×10-2 9.890 000×10-2 VNIR-2 9.042 234×10-3 9.960 000×10-2 
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表 2 水深值与波段反射率值的相关系数
Table 2. The correlation coefficient between water depth and band reflectance
波段 Coastal Blue Green Yellow Red Red Edge NIR-1 NIR-2 相关系数 -0.375 -0.365 -0.439 -0.470 -0.474 -0.467 -0.471 -0.469
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表 3 BP网络训练参数
Table 3. BP training parameters
隐含层个数 隐含层函数 输出层函数 R 16 tansig logsig 0.997 05 17 tansig purelin 0.996 65 17 tansig logsig 0.997 06 17 logsig logsig 0.996 73 18 tansig logsig 0.996 85
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表 4 BP与RBF网络模型
Table 4. BP and RBF neural network model
网络模型 R2 MAE(m) RMSE(m) t(s) BP 0.955 6 1.149 3 1.832 1 21 RBF 0.995 0 0.406 7 0.892 2 9
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