Abstract
The real-time, accurate, and refined monitoring of urban green space status information is of great significance in the construction of urban ecological environment and the improvement of urban ecological benefits. The high-resolution technology can provide abundant information of ground objects, which makes the information of urban green surface more complicated. The existing classification methods are challenging to meet the classification accuracy and automation requirements of high-resolution images. This paper proposed a deep learning classification method for urban green space based on phenological features constraints in order to make full use of the spectral and spatial information of green space provided by high-resolution remote sensing images (GaoFen-2) in different periods. The vegetation phenological features were added as auxiliary bands to the deep learning network for training and classification. We used the HRNet (High-Resolution Network) as our model and introduced the Focal Tversky Loss function to solve the sample imbalance problem. The experimental results show that the introduction of phenological features into HRNet model training can effectively improve urban green space classification accuracy by solving the problem of misclassification of evergreen and deciduous trees. The improvement rate of F1-Score of deciduous trees, evergreen trees, and grassland were 0.48%, 4.77%, and 3.93%, respectively, which proved that the combination of vegetation phenology and high-resolution remote sensing image can improve the results of deep learning urban green space classification.
Highlights
Urban green space is an essential part of the urban ecosystem, which is closely related to the urban ecological environment [1], biodiversity [2], human physical and mental health [3], quality of life of residents [4], and social security [5]
In order to prove the effectiveness of the proposed method, it was compared with SVM, DeepLabv3+, SegNet, U-Net, ResUNet, and High-Resolution Network (HRNet) without phenological features
A deep learning classification method for urban green space that is based on the Focal Tversky Loss and phenological features is constructed to solve the problems of incomplete extraction of green space types and inaccurate boundary extraction from high-resolution remote sensing images
Summary
Urban green space is an essential part of the urban ecosystem, which is closely related to the urban ecological environment [1], biodiversity [2], human physical and mental health [3], quality of life of residents [4], and social security [5]. The primary classification unit is no longer a single pixel, but a “homogeneous and uniform” polygonal object These methods can effectively avoid the “salt and pepper effect” in the pixel based classification method, but the selection of object segmentation parameters and feature optimization need repeated experiments [18,19,20]. These methods require more manual intervention and is difficult to meet the needs of the big data era. The deep learning technology provides a new intelligent interpretation method for urban green space classification in the future [22]
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