SOFNet: SAR-Optical Fusion Network for Land Cover Classification

The objective of this research is to realize automatic land cover classification from synthetic aperture radar (SAR) and multispectral remote sensing imagery. We develop a SAR-optical fusion network (SOFNet) with the symmetric cross entropy (SCE) loss to utilize both the SAR and optical information in a novel deep neural network. The proposed framework has been trained on the public SEN12MS dataset and tested on the 2020 IEEE-GRSS Data Fusion Contest (DFC2020) dataset. Experimental results show that our approach takes full advantage of multimodal information and outperforms the state-of-the-art convolutional architectures.