Abstract
We intend to fill the methodological-level gaps which exist in the theory of imaging radar (IR) for remote sensing (RS) systems by addressing a novel look at RS imaging as an ill-conditioned inverse problem with model uncertainties. We extend the theory presented in previous studies by developing the fused Bayesian-regularization method for RS image formation subject to the projection-type regularization constraints imposed on the solution. Next, we propose to employ neural network-based-processing for efficient implementation of the developed radar image enhancing algorithms and include some simulation examples to illustrate the overall performances of the proposed approach. Our study is intended to establish the foundation to assist in understanding the basic theoretical aspects of the multi-level (Bayesian-regularization-neural-network-computing) optimization of signal processing techniques for enhancing RS imagery.
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