摘要
针对遥感图像分类的特点 ,提出一种基于模糊小脑模型神经网络的遥感图像分类算法。首先阐述小脑模型神经网络的工作原理 ,然后将模糊理论引入小脑模型神经网络 ,提出一种能反映人脑认知的模糊性和连续性的模糊小脑模型神经网络 ,并将其应用于遥感图像分类。实验结果表明 ,这种基于模糊小脑模型神经网络的分类器经过训练后 ,可应用于遥感图像的分类 。
Considering the features of remote sensing images, we proposed a remote sensing image classification algorithm using Fuzzy Cerebellar Model Articulation Controller (FCMAC) neural network. First, the principle of Cerebellar Model Articulation Controller(CMAC) neural network is described. Then, fuzzy theory is introduced into CMAC and a FCMAC neural network is brought forward. The proposed FCMAC neural network reflects the fuzziness and continuity of human cerebella and is used to remote sensing image classification. Experimental results show that the FCMAC neural network classifier can be used in remote sensing image classification, and its classification precision is superior to that of the conventional MLC algorithm.
出处
《测绘学报》
EI
CSCD
北大核心
2002年第4期327-332,共6页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金资助项目 ( 6 0 0 750 0 8)
关键词
模糊小脑模型
神经网络
遥感图像
分类
传感器
Cerebellar Model Articulation Controller
Fuzzy Cerebellar Model Articulation Controller
neural network
remote sensing image classification
