中文摘要：

基于随机森林算法,采用多季节、多时相光谱信息、纹理信息和地形信息进行分类研究,选出最佳分类方案对农耕区土地利用信息进行提取,并利用随机森林算法对所有特征变量进行降维,将降维后的变量分别用于随机森林、支持向量机和最大似然分类法,分析不同分类方法对农耕区土地利用类型提取的适用性。研究结果表明：基于随机森林算法的多源信息综合分类方案进行土地利用信息提取效果最佳,总体精度85.54%,Kappa系数0.835 9;利用随机森林算法进行特征选择可以在有效降低数据维度的同时保证分类精度;3种分类方法中,随机森林算法总体分类精度81.08%,分别较支持向量机和最大似然法高9.46%和5.27%。利用随机森林分类法结合多源信息能够有效对农耕区土地利用类型进行分类,为土地类型的划分提供了快捷可行的方法。

英文摘要：

Land use classification plays an important role in adjusting land structure and developing land resources reasonably,especially in the farming area. The objective of this research is to choose an appropriate method to classify land use type in the farming area. A new classification method,random forest（ RF） classifier,was applied to make land use mapping in agricultural cultivation region with multisource information,including multi-seasonal spectrum,texture and topographic information. The best classification scheme was chosen to extract land use information,and RF algorithm was used to reduce the dimension of characteristics variables. The RF algorithm, support vector machine, and maximum likelihood classification were used to map agricultural land use,and the applicability of these three different classification methods was analyzed. The result shows that RF classification of land use classification with multi-source information effects best,the overall accuracy and Kappa coefficient are85. 54% and 0. 835 9 respectively. Feature selection method from RF algorithm can effectively reduce the data dimension and ensure the accuracy of classification at the same time. Compared with these three classification methods, RF algorithm performs the highest overall accuracy of 81. 08%, which is respectively 9. 46% and 5. 27% higher than support vector machine and maximum likelihood classification.It is an effective scheme that makes land use classification in the farming area using RF classifier with multi-source information. It provides a fast and feasible method for the division of land use types.