Knowledge—Based Classification in Automated Soil Mapping
ZhouBin, WANGRen-Chao · Acta Scientiarum Naturalium Universitatis Sunyatseni · 2003
A machine-learning approach was developed for automated building of knowledge bases for soil resourcesmapping by using a classification tree to generate knowledge from training data. With this method, buildinga knowledge base for automated soil mapping was easier than using the conventional knowledge acquisitionapproach. The knowledge base built by classification tree was used by the knowledge classifier to perform thesoil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal imagesand GIS data. To evaluate the performance of the resultant knowledge bases, the classification results werecompared to existing soil map based on a field survey. The accuracy assessment and analysis of the resultantsoil maps suggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.