Remote Sensed Images Fusion and Lake Water Quality Identification Based on Neural Networks and Evidence Theory
基于神经网络-证据理论的遥感图像数据融合与湖泊水质状况识别

In order to identify the lake water quality accurately, this paper presents a method for remote sensed image fusion based on neural networks and evidence theory. This method firstly employs a neural network for each remote sensed image and then normalizes the output of neural networks. After that, D S evidence theory is used to fuse with results from all the neural networks, resulting in the water quality evaluation. The proposed method is applied to the water quality of Taihu lake. The developed approach to water quality identification has the two features:(1) low fault tolerance; and (2) high reliability as multi source water quality data are fused.