Improved SAR Imaging via Cross-Learning From Camera Images

In this article, we propose a novel concept of cross-learning in order to improve synthetic aperture radar (SAR) images by learning from the camera images, in the manifold domain. We present multilevel abstraction approaches to materialize knowledge transfer between these two very different modalities (i.e., the radar and the camera), namely, a canonical correlation analysis-based approach and a manifold alignment-based approach. We provide experimental results on real data, along with qualitative as well as quantitative analyses, to validate the proposed methodologies.