Abstract
In this study, a Local Climate Zone (LCZ) classification framework is established using a Densenet based embranchment Convolutional Neural Network (CNN). Both synthetic aperture radar (SAR) and multispectral data are employed for feature fusion, specifically, considering about the difference in imaging mechanism between SAR and multispectral data, features from both resources are extracted in different branches separately according to the physical properties of each band. Significant accuracy improvement can be achieved when evaluate the proposed method by Sentinel-1 and Sentinel-2 dataset, and the comparison results show the superiority of the proposed embranchment CNN framework over the conventional methods.
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