1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China 2. National Engineering Laboratory for Integrated Air-Space-Ground-Ocean Big Data Application Technology, Beijing 100101, China 3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China 4. Ministry of Housing and Urban-Rural Development of the People’s Republic of China, Beijing 100101, China 5. Guangdong Engineering Research Center of Water Environment Remote Sensing Monitoring, Guangzhou 510275, China 6. Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Guangzhou 510275, China
The efficient and accurate extraction of urban green space (UGS) is of great significance to land planning and construction. The application of deep learning semantic segmentation algorithm to remote sensing image classification is a new exploration in recent years. This paper describes a multilevel architecture which targets UGS extraction from GF-2 imagery based on DeepLabv3plus semantic segmentation network. Through Atrous Spatial Pyramid Pooling (ASPP) and other modules of the network, high-level features are extracted, and data set creation, model training, urban green space extraction and accuracy evaluation are completed relying on the architecture. The accuracy evaluation shows that DeepLabv3plus outperforms the traditional machine learning methods, such as maximum likelihood (ML), support vector machine (SVM), random forest (RF) and other four semantic segmentation networks (PspNet, SegNet, U-Net and DeepLabv2), allowing us to better extract UGS, especially exclude interference of farmland. Through accuracy evaluation, the proposed architecture reaches an acceptable accuracy, with overall accuracy being 91.02% and F Score being 0.86. Furthermore, the authors also explored the portability of the method by applying the model to another city. Overall, the automatic architecture in this paper is capable of excluding farmland pixels'interference and extracting UGS accurately from RGB high spatial RS images, which provides reference for urban planning and managements.
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2020, Vol. 32 
), 邓孺孺1,5,6, 梁业恒1, 熊龙海1
