A Method to Retrieve the Oceanic Chlorophyll-a Concentrations in Case I Water Based on Artificial Neural Network
基于人工神经网络的一类水域叶绿素—a浓度反演方法

A method to retrieve the oceanic chlorophyll concentrations in Casse I water based on Aritificial Neural Network (ANN) is presented. The ANN used in this paper is a three_layer feed forward back_propagation network which has 4 neurons in the input layer (corresponding to the ratios of the remote sensing reflectances at 4 wavelengths: 412 nm, 443 nm, 490 nm, 510 nm to the remote sensing reflectance at 555 nm), 5 neurons in the hidden layer and one neuron for the output layer (Corresponding to chlorophyll concentration). The training data set and testing data set of the ANN come from SeaBAM data base. 70% of the 919 stations in SeaBAM was used for training data set, the other 30% used for testing data set. At the end, the retrieved results from ANN and from the empirical algorithms were compared. The results show that the accuracy of the ANN is better than the cubic empirical algorithms used widely.