Enhanced compressed sensing 3D SAR imaging via cross-modality EO-SAR joint-sparsity priors

Abstract

We introduce a compressed sensing technique for leveraging prior electro-optic (EO) imagery to improve 3D synthetic aperture radar (SAR) imaging performance. Specifically, we build on existing iterative reconstruction algorithms by guiding the reconstruction process with a joint-sparsity regularization term that captures the complementary structural information shared between EO and SAR via a sparsifying transform in the 3D image domain. We demonstrate this approach using the wavelet transform, the non-uniform Fast-Fourier transform (NUFFT), and optimizers built on autograd utilizing the 2004 AFRL Gotcha SAR dataset, with complementary EO imagery collected from the 2013 Minor Area Motion Imagery (MAMI) collection and more recent (2016) satellite collections over the same area. Results indicate significant improvements in 2D and 3D imaging performance via incorporation of the cross-modality EO prior, which we attribute to the convex problem formulation.