MEMBERSHIP FUNCTION – ARTMAP NEURAL NETWORKS

TASK Quarterly

JAN VASˇCˇAK¹  MARCEL HRIC¹  PETER SINCˇAK² 

[1] Faculty of EE and Informatics Technical University, Center for Intelligent Technologies;Siemens AG, Vienna PSE Department, ECANSE working group;

关键词: pattern recognition principles;    classifier design;    classification accuracy assessment;    contingency tables;    backpropagation neural networks;    fuzzy BP neural networks;   

DOI  :  

来源: DOAJ

【 摘 要 】

The project deals with the application of computational intelligence (CI) tools for multispectral image classification. Pattern recognition scheme is a global approach where the classification part is playing an important role to achieve the highest classification accuracy. Multispectral images are data mainly used in remote sensing and this kind of classification is very difficult to assess the accuracy of classification results. There is a feedback problem in adjusting the parts of pattern recognition scheme. Precise classification accuracy assessment is almost impossible to obtain, being an extremely laborious procedure. The paper presents simple neural networks for multispectral image classification, ARTMAP-like neural networks as more sophisticated tools for classification, and a modular approach to achieve the highest classification accuracy of multispectral images. There is a strong link to advances in computer technology, which gives much better conditions for modelling more sophisticated classifiers for multispectral images.

【 授权许可】

Unknown