Him-Net: A New Neural Network Approach For Sar And Optical Image Template Matching

SAR and optical images provide highly complementary information about observed scenes. The integrated use of these two data is desired in many data fusion tasks. However, traditional similarity methods cannot correctly match SAR and optical images due to the significant non-linear radio-metric difference between them. This paper proposed a template matching neural network based on stereo matching for SAR and optical image matching. Unlike the classical template matching methods doing feature extraction and similarity calculating separately, our network is a complete end-to-end approach, which allows optimizing the matching between the SAR and optical images through training. Moreover, a heatmap loss function is designed for image template matching, and better result is obtained. Our experiments confirmed our proposed network advantage over the state-of-the-art similarity approaches (such as NCC, CARMI, DeepMatch, and QATM) and superior matching performance.