A Dynamic End-to-End Fusion Filter for Local Climate Zone Classification Using SAR and Multi-Spectrum Remote Sensing Data

Local Climate Zone (LCZ) classification is potentially popular because of its extensive applications. Recently, data from different remote sensors including synthetic aperture radar (SAR) and multi-spectrum are employed for LCZ classification. However, different bands in SAR and multi-spectrum are difficult to fuse because of their various physical properties. In this paper, an dynamic end-to-end fusion filter is proposed. Firstly, a convolutional neural network (CNN) based dynamic filter network (DFN) is introduced to integrate different bands in SAR and multi-spectrum data, which enhances the fusion accuracy by a flexible dynamic operation. Then the filter is used for feature extraction, hence improve the performance of the classifier. The proposed method is evaluated using Sentinel-1 and Sentinel-2 dataset and the improvement of accuracy shows the superiority of the proposed dynamic data fusion approach.