太阳成8722(中国)有限公司-GREEN NO.1

标题: Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

作者: Renfei Yang 1,Fang Luo 2,Fu Ren 1,3,Wenli Huang 1,3,4,*ORCID,Qianyi Li 2,Kaixuan Du 1 andDingdi Yuan 2

来源出版物: ISPRS Int. J. Geo-Inf   卷:11   期:2  文献号: 131 DOI: 10.3390/ijgi11020131   出版年: 14 February 2022  

摘要: Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities.

作者关键词: urban wetland; scene classification; DenseNet121; standard deviation ellipse; Shenzhen  

地址:

1.School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China

2.Planning and Natural Resources Survey Center of Shenzhen Municipality, Shenzhen 518034, China

3.Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan 430079, China

4.Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China

通讯作者地址: Huang, Wenli(通讯作者)

School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;

Key Laboratory of GIS, Ministry of Education, Wuhan University, Wuhan 430079, China;

Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China

电子邮件地址:  

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