运用分类树进行土壤类型自动制图的研究

提供了一种基于机器学习的方法来自动建立针对土壤资源制图的规则库。以浙江省龙游县研究区为例,将已有的土壤图与地质图、土地利用现状图、DEM及其派生属性、双时相的TM卫星数据相结合,使用分类树算法从训练数据中生成该地区土壤制图的规则知识,并进行了研究区土壤类型的知识分类。这种建立土壤自动制图知识库的方法要比传统的知识获取方法更为简便易行。精度评价结果表明,所建立的知识库对于研究区的大部分土壤类型的预测是可行的。

Automated Soil Mapping by Using Classification Tree Algorithm

A machine-learning approach to automated building of knowledge bases for soil mapping was presented. Classification tree algorithm was applied to generate knowledge from training data. With this method, building a knowledge base for automated soil mapping is 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 area, Zhejiang Province, China using Landsat TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification result was compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggest 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.