%0 Journal Article
%T High-Rank Artificial Neural Network Algorithm for Classification of Hyperspectral Image Data<br>用于高光谱遥感图象分类的 一种高阶神经网络算法
%A XIONG Zhen
%A TONG Qing-xi
%A ZHENG Lan-fen
%A <br>熊 桢
%A 童庆禧
%A 郑兰芬
%J 中国图象图形学报
%D 2000
%I 
%X The BP neural network is widely used for classification of remote sensing image data nowadays. But it has the usual shortcomings of multilayer sensor neural network too: the question about the number of crytic layer and the number of crytic layer node, the question about local minimum, the question about training speed, and so on. In order to solve the questions thoroughly, a sort of classification algorithm of high rank neural network is developed in this research. This algorithm has not crytic layer, so it hasn't the question about the number of crytic layer and the number of crytic layer node. It's interface of model classification is nonlenear, so the question about local minimum is solved thoroughly. It's training speed is faster and the precision of model classification is greater than that of the BP neural network algorithm. In this article, the structure, flow chart and course control of this algorithm is introduced detailedly. Using the hyperspectral data in the destrict of Shahe town, Beijing city, an experiment is done and a excellent result is gained. The classification precision of training sample and the classification precision of test sample are all 100 percent. It is proved that the algorithm of high rank neural network has great advantages than other algorithms of neural network in structure, speed and precision.
%K High-rank neural network
%K Classification precision
%K Pattern recognition<br>高阶神经网络
%K 分类精度
%K 模式判别
%K 遥感图象分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=2D32E5B86873F79E448A98B5375E66B8&yid=9806D0D4EAA9BED3&vid=94C357A881DFC066&iid=38B194292C032A66&sid=E0F6F365E4766526&eid=BC084ACE66B62CC8&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=19&reference_num=0