Remote Sensing Imagery and Signature Fields Reconstruction Via Aggregation of Robust Regularization with Neural Computing

The robust numerical technique for high-resolution reconstructive imaging and scene analysis is developed as required for enhanced remote sensing with large scale sensor array radar/synthetic aperture radar. First, the problem-oriented modification of the previously proposed fused Bayesianregularization (FBR) enhanced radar imaging method is performed to enable it to reconstruct remote sensing signatures (RSS) of interest alleviating problem ill-poseness due to system-level and model-level uncertainties. Second, the modification of the Hopfield-type maximum entropy neural network (NN) is proposed that enables such NN to perform numerically the robust adaptive FBR technique via efficient NN computing. Finally, we report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.