Aggregated Conditional Regularized Enhancement of Environmental Imagery: Numerical Techniques and Simulation Study

Y. Shkvarko and R.B. Garibay (Mexico)


Imaging, conditional optimization, regularization, computer simulation.


In this paper, we develop and test through the extended simulation study the conditional constrained optimization regularization approach to solving the problems of reconstructive/enhanced remote sensing (RS) imaging as required for environmental monitoring and resource management. The proposed aggregated conditional regularized filtering (ACRF) method for reconstructive environmental imaging employs the conditional iterative Lagrange optimization technique incorporating the system-level constraints imposed on the desired solution. The innovative contribution of the present study consists in developing and computational testing of the ACRF reconstructive imaging technique that admits an efficient numerical iterative implementation. The simulations were performed via designing the software package that utilizes the MATLAB 7.0 computational environment. The presented results of simulation study verify the efficiency of the developed and tested constrained regularized reconstructive RS imaging technique.

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