A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2020-11, Vol.12 (22), p.3845
• Main Authors: Xu, Zhiyu, Zhou, Yi, Wang, Shixin, Wang, Litao, Li, Feng, Wang, Shicheng, Wang, Zhenqing
• Format: Article
• Language: English
• Subjects: Accuracy; Automation; Chlorophyll; Classification; Climate change; Deciduous trees; Deep learning; Evergreen trees; Focal Tversky Loss; Grasslands; Green infrastructure; High resolution; high-resolution remote sensing images; HRNet; Image classification; Image resolution; Leisure; Machine learning; Maximum likelihood method; Neural networks; Open spaces; Parks & recreation areas; Phenology; Remote sensing; Semantics; Spatial data; Training; Trees; Urban areas; Urban environments; urban green space classification; Urban planning; Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12223845

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.