Empirical analysis of target responses for understanding SAR to EO translations

Abstract

Image-to-image translation methods aim to convert an image from its native source domain to a target domain. This is a common technique when the target domain's phenomenology is more amenable to a certain task than the source domain. An example of this practice is Synthetic Aperture Radar (SAR) to electro-optical (EO) translation for 3D reconstruction. Techniques in 3D reconstruction have been shown to be effective on EO imagery. A common practice is to translate SAR imagery to the EO domain in order to form 3D reconstructions from SAR imagery. The translation algorithms ultimately map specular SAR responses to diffuse EO responses. While previous work supports the effectiveness of deep neural networks for such a translation, the black-box nature of the trained models does not offer explainability towards the effectiveness of the SAR to EO translations. This work aims to offer explainability for SAR to EO translations via direct comparison of facet responses found in ray-tracing based simulations given equivalent target and sensor geometry. Further analysis of these target responses is conducted in order to understand scenarios where SAR to EO translations is expected to be effective and ineffective.