• 遥感科学与应用技术 •

### 融合高度特征的高分遥感影像多尺度城市建筑类型分类

1. 1. 福州大学数字中国研究院（福建）,福州 350108
2. 空间数据挖掘与信息共享教育部重点实验室,福州 350108
3. 卫星空间信息技术综合应用国家地方联合工程研究中心,福州 350108
• 收稿日期:2021-07-01 修回日期:2021-08-26 出版日期:2021-11-25 发布日期:2022-01-25
• 通讯作者: *李蒙蒙（1988— ）,男,山东临沂人,博士,助理研究员,研究方向为高分辨率遥感图像智能处理、机器学习、城市土地利用分类。E-mail: mli@fzu.edu.cn
• 作者简介:储国中（1997— ）,男,安徽安庆人,硕士生,研究方向为遥感技术与应用。 E-mail: N195520004@fzu.edu.cn
• 基金资助:
国家自然科学基金项目(42001283)

### Integrating Height Features for Multi-scale Urban Building Type Classification from High-Resolution Remote Sensing Images

CHU Guozhong1,2,3(), LI Mengmeng1,2,3,*(), WANG Xiaoqin1,2,3

1. 1. The Academy of Digital China (Fujian), Fuzhou University, Fuzhou 350108, China
2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou 350108, China
3. National ＆ Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou 350108, China;
• Received:2021-07-01 Revised:2021-08-26 Online:2021-11-25 Published:2022-01-25
• Contact: * LI Mengmeng, E-mail: mli@fzu.edu.cn
• Supported by:
National Natural Science Foundation of China, No(42001283)

Abstract:

Urban building type information is crucial to many urban applications such as the identification of urban functional areas and estimation of urban environmental variables. This paper presents a new method to extract urban building types using multi-scale features and integrating height features derived from high resolution remote sensing images. We first conduct an image semantic segmentation to extract building and shadow objects from remote sensing images, and then estimate the height of buildings based upon the directional relationship of a building object and its shadow information. Following multi-scale image analysis concept, we extract a series of multi-scale features regarding the height, geometry, and spatial structure of building objects. Last, we use a machine learning method based upon random forest to classify building types. We also analyze the impact of different spatial units of building types on classification results. Experiments were conducted in Fuzhou, Fujian province, China, using a Chinese GF-2 satellite images acquired on February 18, 2020. Our results show that: (1) The overall accuracy of building type classification combined with multi-scale features reached 82.98%, and the kappa coefficient was 0.77, which was better than other conventional methods, namely a Multi-scale Classification Without Height Features (MCNH), a Single-scale Classification Incorporating Height Features (SC), and a Single-scale Classification Without Height Features (SCNH) in this paper; (2) The classification accuracy of middle-low residential buildings and high-rise commercial and residential buildings was improved by adding height features. Compared with classification results without using height features, the overall accuracy was improved by 11.28%; (3) The fusion of image features at multiple scales can reduce the misclassification of adjacent buildings into dense buildings. Compared with a single-scale classification method, the proposed method improved overall accuracy by 2.77%. We conclude that the use of high-resolution remote sensing images provides an effective strategy to estimate building heights based upon shadow information and improves the classification accuracy of urban building types, particularly when detailed digital surface model data are absent. In addition, the fusion of multi-scale image features can improve the characterization of complex building types in urban areas and the subsequent classification accuracy accordingly. Nevertheless, we also observed that the results of classified building types were affected by the initial extraction of building information from high resolution remote sensing images, implying that a further improvement of building type classification can be done by improving the extraction methods, e.g., using a more advanced semantic segmentation model.