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.