精准获取并识别棚户区的空间分布及形态，对改善人居环境、优化城市空间结构具有重要意义。传统的实地调查方法耗时费力，以上海杨浦区南部的棚户区为例，从高空间分辨率影像中提取光谱、纹理和结构特征作为输入数据，提出了基于机器学习算法的高分遥感影像的棚户区提取方法。首先，综合比较最近邻（KNN）、逻辑回归（LR）、支持向量机（SVM）、随机森林（RF）、集成学习（EL）5种机器学习识别方法的适用性，确定最优分类器。其次，基于网格（50m×50m）对高分影像进行特征提取，并对特征网格分类、构建分类图像特征数据集。最后，通过图像特征进行棚户区识别，并评估5种机器学习算法在城市地区识别棚户区的能力及应用。结果表明：监督机器学习方法在提取棚户区的精度方面基本能够满足棚户区的研究及实际应用，但综合考量分类结果、分类精度和运行效率，EL算法Kappa系数为73.0%、总体精度为97.27%、召回率为79.02%，均高于其它算法，且漏分错误最少，能够更加完整、准确地完成棚户区信息提取。当考虑运行效率时，LR算法识别速度明显高于其它算法，更适用于大范围棚户区的使用需求。监督学习识别方法不仅可应用于高分影像特征识别，在遥感监测、城市规划和测绘等方面也具有较大的应用潜力。

Accurately extracting and identifying the spatial distribution and form of slum is of great significance to improving the living environment and optimizing urban spatial structure. Traditional field investigation methods are time-consuming and laborious. As slums in the southern Yangpu District of Shanghai as the study area， spectral， textural and structural features from high-resolution remote sensing images as input data， this paper proposed an identification method for the slum using Machine Learning （ML） algorithms. Firstly， K-Nearest Neighbor （KNN）， Logistic Regression（LR）， Support Vector Machine（SVM）， Random Forest （RF） and Ensemble Learning（EL） algorithms were compared comprehensively to determine the optimal classifier. Secondly， features were extracted from high-resolution images based on the grid of 50 m×50 m. Then the feature grid was classified and the feature dataset was constructed. Finally， the slum area was identified by image features， and the ability and application of five ML methods in urban area are evaluated. Results showed that supervised machine learning methods could basically meet the research and practical application of slums identification. In terms of the classification results， classification accuracy and operation efficiency， the Kappa coefficient of EL algorithm was 73.0%， the overall accuracy was 97.27%， and the recall rate was 79.02%， which were all higher than other algorithms， and the omission errors were the least. Therefore， the EL algorithm could complete the information extraction of slums more completely and accurately. When considering the operating efficiency， the LR algorithm had a higher identification speed than other algorithms and was more suitable for the use of a large range of slums. Moreover， ML methods could not only be used for features extracting in high-resolution images， but also had great application potential in remote sensing monitoring， urban planning and mapping.