After decades of research, automatic synthetic aperture radar (SAR)-optical registration remains an unsolved problem. SAR and optical satellites utilize different imaging mechanisms, resulting in imagery with dissimilar heterogeneous characteristics. Transforming and translating these characteristics into a shared domain has been the main challenge in SAR-optical matching for many years. Combining the two sensors will improve the quality of existing and future remote sensing applications across multiple industries. Several approaches have emerged as promising candidates in the search for combining SAR and optical imagery. In addition, recent research has indicated that machine learning-based approaches have great potential for filling the information gap posed by utilizing only one sensor type in Earth observation applications. However, several challenges remain, and combining them is a multi-step process where no one-size-fits-all approach is available. This article reviews traditional, state-of-the-art, and recent development trends in SAR-optical co-registration methods.

Accurate synthetic aperture radar (SAR)-optical matching is essential for combining the complementary information from the two sensors. However, the main challenge is overcoming the different heterogeneous characteristics of the two imaging sensors. In this article, we propose an end-to-end machine learning pipeline inspired by recent advances in image segmentation. We develop a siamese multi-scale attention-gated residual U-Net for feature extraction from satellite images. The siamese architecture shares weights and transforms the heterogeneous images into a homogeneous feature space. Fast Fourier transform (FFT) is used to compute the cross-correlation between the feature maps and produce a similarity map. A contrastive loss is introduced to aid the training procedure of the model and maximize the discriminability of the model. The experimental results on a benchmark dataset show that the proposed method has superior matching accuracy and precision compared to other state-of-the-art methods.