%0 Journal Article
%T A Neural Network Model for Water Quality Retrievals Using Knowledge and Remote-sensed Image<br>基于知识和遥感图像的神经网络水质反演模型
%A SHI Ai-ye
%A XU Li-zhong
%A YANG Xian-yi
%A HUANG Feng-chen
%A SHI Ai-ye
%A XU Li-zhong
%A YANG Xian-yi
%A HUANG Feng-chen
%A SHI Ai-ye
%A XU Li-zhong
%A YANG Xian-yi
%A HUANG Feng-chen
%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 2006
%I 
%X 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.
%K remotely sensed image
%K water quality retrievals
%K BP neural network
%K knowledge<br>遥感图像
%K 水质反演
%K BP神经网络
%K 知识
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=628FA9C763FF7C2E&yid=37904DC365DD7266&vid=708DD6B15D2464E8&iid=E158A972A605785F&sid=68FF5306A8F9C315&eid=652E7E360EBE3082&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=6&reference_num=11