• ISSN 1004-0323     CN 62-1099/TP
• 联合主办：中国科学院遥感联合中心
• 中国科学院兰州文献情报中心
• 中国科学院国家空间科学中心
 遥感技术与应用  2022, Vol. 37 Issue (4): 811-819    DOI: 10.11873/j.issn.1004-0323.2022.4.0811
 深度学习专栏

1.中国地质大学 地球科学与资源学院，北京 100083
2.中国科学院空天信息创新研究院，北京 100094
3.广州市城市规划勘测设计研究院，广东 广州 510060
4.广东省城市感知与监测预警企业重点实验室，广东 广州 510060
5.北京林业大学 园林学院，北京 100083
Impervious Surface Extraction from High-resolution Images based on Multi-scale Feature Fusion in U-Net Network
Jing Wang1(),Shuai Gao2(),Liang Guo3,4,Yun Wang5
1.School of Earth Sciences and Resources，China University of Geosciences，Beijing 100083，China
2.Aerospace Information Research Institute，Chinese Academy of Sciences，Beijing 100094，China
3.Guangzhou Urban Planning & Design Survey Research Institute，Guangzhou 510060，China
4.Guangdong Enterprise Key Laboratory for Urban Sensing，Monitoring and Early Warning，Guangzhou 510060，China
5.The College of Forestry of Beijing Forestry University，Beijing 100083，China
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Abstract:

As an important index for monitoring urban ecological environment， the extraction of impervious surface is of great significance. Due to the complexity of urban surface and the need of detailed urban management， it is urgent to extract high-precision urban impervious surface. However， it is difficult to extract high precision impervious surface based on traditional methods. Deep learning method has gradually become a new method of remote sensing image feature extraction because of its characteristics of automatic image feature extraction. Based on this， this paper uses the U-Net deep learning method based on multi-scale feature fusion to improve the semantic segmentation accuracy， and carries out the research on the accurate extraction of impervious surface from high resolution remote sensing images.The residual module is introduced instead of convolution to deepen the network and extract more image features， the pyramid pooling module is added to enhance the network's ability to resolve complex scenarios. It is beneficial to recover spatial information by combining different scale features with jump connection. In this paper， aerial orthophoto images of Guangzhou were taken as the data source. Through convolutional neural network， the remote sensing image is segmtioned into five types of features： background， others， vegetation， road and building. Verify it with the ground truth value of manual visual interpretation， finally， the impervious surface of the study area was extracted. Experiments show that the overall accuracy and Kappa coefficient of the U-NET model are 87.596% and 0.82， respectively. It is superior to traditional supervised taxonomy， object-oriented taxonomy and classical U-Net model in both qualitative and quantitative aspects. The results show that the model can effectively improve the segmentation accuracy of complex scene images by using the multi-dimensional image feature information， and the segmentation effect is good， which is suitable for the extraction of impervious water from high resolution remote sensing images. The research results in this paper can provide data support for urban environmental monitoring.

Key words: Impervious surface    U-Net    Residual module    Pyramid pooling module    High resolution remote sensing

 : TP79