Cross-Domain Local Climate Zone Classification with Distance Metric Based on Domain Adaptation Method

The local climate zone (LCZ) classification system provides a standardized framework for presenting the morphological and functional characteristics of cities, especially for urban climate studies. The LCZ classification scheme is now widely used in urban heat island climate studies, urban planning, weather forecasting and other studies. Previous studies have generally been based on traditional machine learning and manual feature design for LCZ classification. Although deep learning methods perform well in LCZ classification, the generalization ability is still insufficient when facing large scale mapping, especially for cross-domain classification where the source and target domains have significant differences in feature distribution. In this paper, a subdomain adaptation feature alignment architecture (SAFA) is proposed to reduce inter-domain differences. In SAFA, convolutional neural network as a feature extractor in the framework, subdomain adaptive layers embedded in the network aligning features in the source and target domains. The proposed architecture is evaluated on a publicly available dataset. The experimental results show that the proposed architecture improves the overall classification performance of LCZ and NDVI helps to improve the accuracy of natural categories.