%0 Journal Article
%T Remote Sensed Images Fusion and Lake Water Quality Identification Based on Neural Networks and Evidence Theory<br>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
%A SHI Ai ye
%A XU Li zhong
%A YANG Xian yi
%A HUANG Feng chen
%A <br>石爱业
%A 徐立中
%A 杨先一
%A 黄凤辰
%J 中国图象图形学报
%D 2005
%I 
%X 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.
%K remote
%K senssed image
%K water quality identification
%K data fusion
%K D
%K S evidence theory
%K neural networks<br>神经网络
%K D-S证据理论
%K 遥感图像数据
%K 识别
%K 行数据
%K 容错性
%K 融合
%K 水质状况
%K 处理
%K 增加
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=99ACDE53FEB9E86D&yid=2DD7160C83D0ACED&vid=F3090AE9B60B7ED1&iid=38B194292C032A66&sid=965F4E89CD0AFC30&eid=389106FB36CA8332&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=5&reference_num=8