摘要
作为一种自组织网络,Kohonen神经网络在遥感影像聚类中得到了广泛的应用。但Kohonen算法具有初始权值随机化、学习率和邻域难以确定等缺陷,并且作为一种聚类算法,难以直接进行监督分类。对Kohonen非监督学习算法进行改进,用最大最小距离法确定初始权值,利用权值误差作为网络训练收敛条件,并将Kohonen算法与奖惩学习结合起来,进行遥感监督分类。最后,选取安徽省寿县附近区域作为实验区,利用环境一号卫星影像(HJ-1B/CCD)进行了分类实验,其总精度为89.48%,Kappa系数为0.87。实验结果表明,改进的Kohonen神经网络遥感监督分类算法,其分类精度优于经典的最大似然法,取得了较为理想的分类结果。
Kohonen algorithm has defects that initial weights are randomized, learning rate and neighborhood are ditticult to determine, and as a clustering algorithm, kohonen algorithm is difficult to be used directly for supervised classification. In this paper, we used max-rain distance method to determine Kohonen Network' initial weights, introduced weight error as the convergence condition of network, and improved Kohonen network' unsupervised-learning algorithm. We then used the im- proved Kohonen' learning algorithm combined with penalty formula to carry out supervised elassitication of remote sensing data. Experiments showed that higher accuracy resulted from this improved algorithm.
出处
《重庆邮电大学学报(自然科学版)》
北大核心
2011年第5期616-620,共5页
Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition)
基金
国家自然科学基金(40801172)
重庆邮电大学青年基金(A2008-21
A2010-16)~~