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摘要： 针对高海拔复杂地形区地貌类型复杂、多样，沟壑纵横、地形破碎等特点，研究快速、有效的土地利用/土地覆被分类方法对土地利用信息获取及更新是非常重要的。以位于黄土高原向青藏高原过渡带的湟水流域为研究区域，基于Landsat 8 OLI影像数据、DEM数据，并结合各种专题特征，在对研究区进行地理分区的基础上，采用人工神经网络、决策树、支持向量机和随机森林4种机器学习方法进行土地利用信息提取并进行精度评价，探索适合于复杂地形区最优的分类方法。研究结果表明：随机森林和决策树的分类精度明显高于支持向量机和人工神经网络。其中随机森林方法的分类精度最高，总体分类精度达85.65%，Kappa系数达0.84。在上述分类基础上，选择随机森林分类方法对Landsat 8全色与多光谱影像融合数据进行进一步的分类研究，总体分类精度达到[JP]86.49%，Kappa系数达0.85。这表明随机森林分类方法在保证分类精度的同时又能获得较高的分类效率，对于复杂地形区土地利用信息提取是非常有效的，数据融合在一定程度上提高了分类精度。 关键词：    土地利用/土地覆被分类；Landsat OLI影像；机器学习；人工神经网络；决策树；支持向量机；随机森林；湟水流域     Abstract:  Aiming at the characteristics of varied and complex geomorphic types，crisscross network of ravines and broken terrain in high altitude complicated terrain regions，it is very important to study and find the rapid and effective land use/land cover classification method for obtaining and timely updating of land use information.Taking the Huangshui river basin located in the transitional zone between the Loess Plateau and the Qinghai-Tibet Plateau as acasestudy area，the objective of this study is to explore a kind of effective information extraction method from comparison of four kinds machine learning methods for complicated terrain regions.based on Landsat 8 OLI satellite data，DEM and combined with various thematic features，on the basis of geographical division of the study area，artificial neural network，decision tree，support vector machine and random forest four machine learning methods for land use information extraction were used to obtain land use data，and confusion matrix was constructed to evaluate classification accuracy.The results showed that the classification accuracies of random forest and decision tree are obviously higher than those of support vector machine and artificial neural network.The random forest method has the highest classification accuracy，the overall classification accuracy is 85.65%，the Kappa coefficient is 0.84.based on the above classification，Random forest classification method was chose to further classify Landsat 8 fusion datafrom panchromatic 15 meter and multispectral 30 meter image，the overall classification accuracy is 86.49% and the Kappa coefficient is 0.85.This indicated that the random forest classification method can obtain higher classification efficiency while ensuring the classification accuracy.It is very effective for the extraction of land use information in complicated terrain regions.Data fusion can improve the classification accuracy to a certain extent.

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