A Neural Network Model for Water Quality Retrievals Using Knowledge and Remote-sensed Image
基于知识和遥感图像的神经网络水质反演模型

In order to improve water quality retrievals of remotely sensed image accurately, this paper puts forward a neural network model for water quality retrievals using knowledge and remotely sensed image. The model uses remotely sensed image data and water quality related knowledge as input of BP neural network, then trains neural network, after that water quality is retrieved by the trained neural network. The proposed model is applied to the water quality retrievals of Tai Lake in China. In experiment, knowledge used includes Tai Lake geography information knowledge and classification knowledge of water quality by interpretation of TM image. The result of experiment shows that the developed model has more accuracy than the routine linear regression model and traditional neural network model.