Evaluation of Various Classifiers on Regional Land Cover Classification in Huabei Area

doi:10.7523/j.issn.2095-6134.2005.6.011

Abstract

Abstract:

Five classification methods which are MLC (Maximum Likelihood Classifier),Parzen window,CART decision tree,BP neural network and Fuzzy ARTMAP neural network are selected to map the land cover of Huabei Area in China using MODIS 250m data.The results show that Parzen window performs best in the five classifiers.And CART and BP have satisfactory accuracy whereas Fuzzy ARTMAP has unexpected bad accuracy in comparison with MLC.CART decision tree has better flexibility and robustness.However,it pursues high accuracy at the cost of the sample size.BP neural network has high accuracy but requires high-quality samples and it is hard to define its net structure parameters.The results also show the classification accuracy difference caused by the size of training samples on MLC,Parzen window and Fuzzy ARTMAP,CART and BP are below 5%,5%～10% and above 10%,respectively.

Key words: MODIS 250m, land cover classification, MLC, Parzen window, CART, BP, Fuzzy ARTMAP

CLC Number:

TP753

LIU Yong-Hong, NIU Zheng, XU Yong-Ming, LI Xiang-Jun. Evaluation of Various Classifiers on Regional Land Cover Classification in Huabei Area[J]. , 2005, 22(6): 724-732.

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