On the fusion strategies of Sentinel-1 and Sentinel-2 data for local climate zone classification

Abstract

Local Climate Zone (LCZ) classification is the most commonly used scheme to analyze how local urban morphology affects the climate of local areas. Classification methods are often based on remote sensing data or on a fusion of several data sources. In this study, the effects of different fusion strategies of optical and synthetic aperture radar (SAR) data on the accuracy of LCZ classifications are investigated. The data processing is implemented with a convolutional neural network (CNN), where until a fusion layer, separate data sources are processed separately on branches. Strategies of splitting the data into branches and the effects of different fusion stages are compared, together with approaches based on sums of independent classifiers. For our setting, the stage of fusion does not seem to have a big influence on the accuracy. The results of this study contribute to a better understanding of cooperative usage of multispectral and SAR data.