We address a new approach to the problem of improvement of the quality of multi-grade spatial-spectral images
provided by several remote sensing (RS) systems as required for environmental resource management with the use of
multisource RS data. The problem of multi-spectral reconstructive imaging with multisource information fusion is stated
and treated as an aggregated ill-conditioned inverse problem of reconstruction of a high-resolution image from the data
provided by several sensor systems that employ the same or different image formation methods. The proposed fusionoptimization
technique aggregates the experiment design regularization paradigm with neural-network-based
implementation of the multisource information fusion method. The maximum entropy (ME) requirement and projection
regularization constraints are posed as prior knowledge for fused reconstruction and the experiment-design regularization
methodology is applied to perform the optimization of multisource information fusion. Computationally, the
reconstruction and fusion are accomplished via minimization of the energy function of the proposed modified multistate
Hopfield-type neural network (NN) that integrates the model parameters of all systems incorporating a priori
information, aggregate multisource measurements and calibration data. The developed theory proves that the designed
maximum entropy neural network (MENN) is able to solve the multisource fusion tasks without substantial complication
of its computational structure independent on the number of systems to be fused. For each particular case, only the
proper adjustment of the MENN's parameters (i.e. interconnection strengths and bias inputs) should be accomplished.
Simulation examples are presented to illustrate the good overall performance of the fused reconstruction achieved with
the developed MENN algorithm applied to the real-world multi-spectral environmental imagery.
In this paper, we propose to unify the Bayesian estimation strategy with the statistical regularization-based techniques for image reconstruction through developing the fused Bayesian-regularization (FBR)method for the high resolution estimation of the spatial spectrum pattern (SSP) of the wave field scattered from the probing surface. The problem is treated as it is required for enhanced radar imaging of the remotely sensed scenes via processing one sampled realization of the SAR trajectory signal. The derived optimal FBR estimator is a nonlinear solution-dependent (thus referred to as an adaptive) algorithm that also permits a concise robust simplication to the non-adaptive easy-to-implement imaging techniques. The optimal and robustified suboptimal SSP estimation algorithms imply formation of the second order sufficient statistics from the SAR trajectory data signals and their smoothing applying the window operators. The new formalism of such the sufficient statistics and windows explaining their adjustment to the metrics in a solution space, a priori nonparametric model of the desired SSP, its correlation properites and imposed regularization constraints is developed. The advantage in using the proposed method is demonstrated through simulations of enhancing the SAR images using a family of the robustified FBR-based imaging algorithms.