As a newly developed classification system, the LCZ scheme provides a research framework for Urban Heat Island (UHI) studies and standardizes the worldwide urban temperature observa- tions. With the growing popularity of deep learning, deep learning-based approaches have shown great potential in LCZ mapping. Three major cities in China are selected as the study areas. In this study, we design a deep convolutional neural network architecture, named Residual combined Squeeze-and-Excitation and Non-local Network (RSNNet), that consists of the Squeeze-and-Excitation (SE) block and non-local block to classify LCZ using freely available Sentinel-1 SAR and Sentinel-2 multispectral imagery. Overall Accuracy (OA) of 0.9202, 0.9524 and 0.9004 for three selected cities are obtained by applying RSNNet and training data of individual city, and OA of 0.9328 is obtained by training RSNNet with data from all three cities. RSNNet outperforms other popular Convolutional Neural Networks (CNNs) in terms of LCZ mapping accuracy. We further design a series of experiments to investigate the effect of different characteristics of Sentinel-1 SAR data on the performance of RSNNet in LCZ mapping. The results suggest that the combination of SAR and multispectral data can improve the accuracy of LCZ classification. The proposed RSNNet achieves an OA of 0.9425 when integrat- ing the three decomposed components with Sentinel-2 multispectral images, 2.44％ higher than using Sentinel-2 images alone.

Lin Zhou;Zhenfeng Shao;Shugen Wang;Xiao Huang

School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan,China;Department of Geosciences,University of Arkansas,Fayetteville,NC,USA

地球空间信息科学学报（英文版）

[1]Lin Zhou;Zhenfeng Shao;Shugen Wang;Xiao Huang-.Deep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery)[J].地球空间信息科学学报（英文版）,2022(03):383-398

standardizes,RSNNet

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