摘要
以多层感知器 (MLP)为例 ,探讨了地学知识与ANN融合进行遥感影像分类的方法。首先对MLP网络结构、学习算法及其改进进行分析 ;然后总结了MLP进行遥感影像分类的一般方法和存在的缺陷 ;发展了基于知识的MLP神经网络遥感影像分类模型 ,并具体利用基于规则的MLP方法进行了遥感土地覆盖分类的实验 ,把获得的结果与传统统计方法和一般ANN方法进行了综合比较 ,获得了有意义的结果。
The Classification of remotely sensed data is the main theme of Remote Sensing Image Understanding and Analysis, while the artificial neural networks (ANN) is one of the latest and most important techniques developed recently in the area of connective artificial intelligence. In this paper, after we made a through study on the structure of the multi_layer perceptron (MLP) and deeply analyzed it's back_propagation (BP) training algorithm, the framework of how to integrate Geo_knowledge with ANN and apply to RS classification is put forward. Firstly, the suggestions of improving the efficiency of BP algorithm, including network architecture selection, use of optimization on learning rate, and assistance with additional data and expert knowledge etc., are presented. Then, after the general approach of ANN based RS image classification is reviewed, the model of integrating Geo_knowledge with ANN for RS image classification is developed with specific experiment of RULE based MLP. Experimental result shows significant improvement in comparison with statistical and traditional ANN classifiers.
出处
《遥感学报》
EI
CSCD
北大核心
2001年第2期122-129,共8页
NATIONAL REMOTE SENSING BULLETIN
基金
中国科学院知识创新项目! :KZCX1 Y 0 2
关键词
人工神经网络
多层感知器
BP学习算法
遥感图像分类
地学知识
artificial neural networks (ANN)
multi_layer perceptron (MLP)
BP algorithm
RS image classification
geo_knowledge
