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摘要： 针对我国南方地区植被类型复杂、地形复杂和地块破碎等原因导致耕地信息提取精度较低问题，提出了一种面向对象和CART决策树结合的复杂条件下耕地面积提取方法。以广西南宁市隆安县与武鸣县地区为研究区，采用Sentinel-2A影像，结合数字高程数据（Digital Elevation Model，DEM）及归一化植被指数（Normalized Difference Vegetation Index，NDVI）等多源数据，利用面向对象分割技术识别地块信息，然后以地块为单位采用CART（Classification And Regression Tree，CART）决策树分类法，依据不同地类的形状、光谱特征，提取研究区的耕地。结果表明：面向对象的CART决策树分类方法分类总体精度和Kappa系数分别为96.1%和0.94，相比较于未加入面向对象分割的CART决策树耕地信息提取总体精度提高Kappa系数提高0.54，面向对象的分割方法有利于减少复杂背景对耕地提取的影响。基于面向对象的CART决策树分类方法相比较于传统方法对研究区耕地信息的提取有较好的精确性，能够提高耕地信息的提取精度。 关键词： Sentinel-2A;  面向对象;  CART决策树分类;  耕地提取     Abstract: In order to solve the problems of low precision of cultivated land information extraction due to complex vegetation types， complex terrain and broken plots in southern China， a method of arable land area extraction under complex conditions of object-oriented and cart decision tree is proposed. Taking Longan County and Wuming County of Nanning City， Guangxi as the study area， using Sentinel-2A image， combining digital elevation data DEM and normalized vegetation index NDVI and other multi-source data， using object-oriented segmentation technology to identify plot information， and then using CART decision tree classification method， according to the shape and spectral characteristics of different land types， the cultivated land in the study area is extracted. The results show that the overall precision and Kappa coefficient of the object-oriented CART decision tree classification method are 96.1% and 0.94， respectively. Compared with the total accuracy of cultivated land information extraction of cart decision tree without object-oriented segmentation， the kappa coefficient is increased by 0.54. The object-oriented segmentation method is beneficial to reducing the influence of complex background on the extraction of cultivated land. Based on the object-oriented CART decision tree classification method， the extraction of the cultivated land information in the research area is better than the traditional method， and the extraction precision of the cultivated land information can be improved.

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