Pedosphere 13(3): 209--218, 2003
ISSN 1002-0160/CN 32-1315/P
©2003 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Knowledge-based classification in automated soil mapping |
| ZHOU Bin and WANG Ren-Chao |
| Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029 (China) |
| ABSTRACT |
| A machine-learning approach was developed for automated building of knowledge bases for soil resources mapping by using a classification tree to generate knowledge from training data. With this method, building a knowledge base for automated soil mapping was easier than using the conventional knowledge acquisition approach. The knowledge base built by classification tree was used by the knowledge classifier to perform the soil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal images and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on a field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by the machine-learning method was of good quality for mapping distribution model of soil classes over the study area. |
| Key Words: classification, classification tree, knowledge-based, rule extracting, soil mapping |
| Citation: Zhou, B. and Wang, R. C. 2003. Knowledge-based classification in automated soil mapping. Pedosphere. 13(3): 209-218. |
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