A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract

With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes a mixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.