Optimization of Multisource Information Fusion for Resource Management with Remote Sensing Imagery: An Aggregate Regularization Method with Neural Network Implementation

摘要

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 fusion-optimization 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.