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A Novel Neural Network Computing Based Way to Sensor and Method Fusion in Harsh Operational Environments

We address a novel neural network computing-based approach to the problem of near real-time feature enhanced fusion of remote sensing (RS) imagery acquired in harsh sensing environments. The novel proposition consists in adapting the Hopfield-type maximum entropy neural network (MENN) computational framework to solving the RS image fusion inverse problem. The feature enhanced fusion is performed via aggregating the descriptive experiment design with the variational analysis (VA) inspired regularization frameworks that lead to an adaptive procedure for proper adjustments of the MENN synaptic weights and bias inputs. We feature on the considerably speeded-up implementation of the MENN-based RS image fusion and verify the overall image enhancement efficiency via computer simulations with real-world RS imagery.