Recently generative adversarial network (GAN) has been explored to multimodal template matching. Existing GAN-based multimodal template matching methods exploit the image generation to transform the multimodal template matching task as the unimodal one. However, image synthesis-based multimodal template matching methods relay on the generated image quality, which is unstable. Towards this end, this paper proposes a feature adversarial network, which maps different modal images into a common subspace and learns the correlation in the subspace. Specifically, the feature mapper is designed to map the multimodal features into intermediate features, and the modality discriminator is proposed to optimize the multimodal intermediate features until they are indistinguishable. Thus, an effective common feature subspace is generated for correlation learning. The experimental results on a public dataset demonstrate the superiority of the proposed method.

Template matching of multi-modal image has been a challenge to image matching, and it is difficult to balance the speed and the accuracy, especially for images with large sizes. Based on this, we propose a stepwise image matching method to achieve a precise location from the coarse-to-fine image matching by utilizing cascaded networks. In the proposed method, a coarse-grained matching network is firstly constructed to locate a rough matching position based on cross-correlating features of optical and SAR images. Specially, to enhance the credible matching position, a suppression network is designed to evaluate for the obtained cross-correlation feature and added into the coarse-grained network as a feedback. Secondly, a fine-grained matching network is constructed based on the obtained rough matching result to gain a more precise matching. In this part, ternary groups are utilized to construct the training samples. Interestingly, we apply the region with a few pixels offset as the negative class, which effectively distinguishes similar neighbourhoods of the rough matching position. Moreover, a modified Siamese network is used to extract features of SAR and optical images, respectively. Finally, experimental results illustrate that the proposed method obtains more precise matching compared with the state-of-the-art methods.