Computational enhancement of large scale environmental imagery: aggregation of robust numerical regularization, neural computing and digital dynamic filtering

We address a new efficient robust optimisation approach to large-scale environmental image reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem-oriented robustification of the previously proposed Fused Bayesian-Regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-model uncertainties. Second, the modification of the Hopfield-type Maximum Entropy Neural Network (MENN) is proposed that enables such MENN to perform numerically the robustified FBR technique via computationally efficient iterative scheme. The efficiency of the aggregated robust regularised MENN technique is verified through simulation studies of enhancement of the real-world environmental images.CINVESTA

Simulation Study of the Unified Bayesian-Regularization Technique for Enhanced Radar Imaging

In this paper, we intend to present the results of extended simulation study of the family of the radar image (RI) formation algorithms that employ the recently developed and investigated fused Bayesian-regularization (FBR) paradigm for high-resolution reconstruction of the spatial spectrum pattern (SSP) of the wavefield sources distributed in the remotely sensed environment. The FBR methodology is based on the aggregation of the Bayesian minimum risk statistical optimal estimation strategy with the descriptive weighted constrained least squares optimization technique that involves the non trivial a priori information on the desired properties of the SSP to be reconstructed from the actually measured data signals. The advantages of the well designed RI experiments (that employ the FBR-based methods) over the cases of poorer designed experiments (that employ the matched spatial filtering as well as the constrained least squares estimators) are investigated trough the simulation study.Cinvesta

Real-Time Reconstruction of Remote Sensing Imagery: Aggregation of Robust Regularization with Neural Computing

The robustified numerical technique for real-time sensor array reconstructive image processing is developed as required for remote sensing imaging with large scale array/synthesized array radars. The addressed technique is designed via performing the regularized robustification of the fused Bayesian-regularization imaging method aggregated with the efficient real-time numerical implementation scheme that employs the neural network computing.CINVESTA

Comparative Study of the Descriptive Experiment Design and Robust Fused Bayesian Regularization Techniques for High-Resolution Radar Imaging

In this paper, we perform a comparative study of two recently proposed high-resolution radar imaging paradigms: the descriptive experiment design regularization (DEDR) and the fused Bayesian regularization (FBR) methods. The first one, the DEDR, employs aggregation of the descriptive regularization and worst-case statistical performance (WCSP) optimization approaches to enhanced radar/SAR imaging. The second one, the FBR, performs image reconstruction as a solution of the ill- conditioned inverse spatial spectrum pattern (SSP) estimation problem with model uncertainties via unifying the Bayesian minimum risk (MR) estimation strategy with the maximum entropy (ME) randomized a priori image model and other projection-type regularization constraints imposed on the solution. Although the DEDR and the FBR are inferred from different descriptive and statistical constrained optimization paradigms, we examine how these two methods lead to structurally similar techniques that may be further transformed into new computationally more efficient robust adaptive imaging methods that enable one to derive efficient and consistent estimates of the SSP via unifying both the robust DEDR and FBR considerations. We present the results of extended comparative simulation study of the family of the image formation/ enhancement algorithms that employ the proposed robustified FBR and DEDR methods for high-resolution reconstruction of the SSP in a virtually real time. The computational complexity of different methods are analyzed and reported together with the scene imaging protocols. The advantages of the well designed SAR imaging experiments (that employ the FBR-based and DEDR-related robust estimators) over the cases of poorer designed experiments (that employ the conventional matched spatial filtering as well as the leaCinvestavUniversidad de Guadalajar

Dynamical Enhancement of the Large Scale Remote Sensing Imagery for Decision Support in Environmental Resource Management

In this study, we address a new efficient robust optimization approach to large-scale environmental RSSS reconstruction/enhancement as required for remote sensing imaging with multi-spectral array sensors/SAR. First, the problem- oriented robustification of the previously proposed fused Bayesian-regularization (FBR) enhanced imaging method is performed to alleviate its ill-poseness due to system-level and model-level uncertainties. Second, we incorporate the dynamic filtration paradigm into the overall reconstruction technique to enhance the quality of the imagery as it is required for decision support in environmental resource management with dynamic RSSS behavior.CINVESTA

Unified Bayesian-Experiment Design Regularization Technique for High-Resolution of the Remote Sensing Imagery

In this paper, the problem of estimating from a finite set of measurements of the radar remotely sensed complex data signals, the power spatial spectrum pattern (SSP) of the wavefield sources distributed in the environment is cast in the framework of Bayesian minimum risk (MR) paradigm unified with the experiment design (ED) regularization technique. The fused MR-ED regularization of the ill- posed nonlinear inverse problem of the SSP reconstruction is performed via incorporating into the MR estimation strategy the projection-regularization ED constraints. The simulation examples are incorporated to illustrate the efficiency of the proposed unified MR-ED technique.Cinvesta

Intelligent Processing of Remote Sensing Imagery for Decision Support in Environmental Resource Management: A Neural Computing Paradigm

In this study, we propose a new neural network (NN) computational paradigm to resolve the resource management decision support (DS) oriented problems based on reconstructive remote sensing (RS) imaging with the use/fusion of multi- sensor systems as required for enhanced DS in environmental resource management and other related fields in DS technologies. The developed NN paradigm addresses a framework for resolving the computational problems related to the end-user DS in environmental monitoring based on the intelligent RS image reconstruction/enhancement.Cinvesta

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 Bayesian- regularization (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 devel- oped method.Cinvesta

Dynamical Post-Processing of Environmental Electronic Maps Extracted from Large Scale Remote Sensing Imagery

A new intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of physical characteristics of environmental electronic maps extracted from the large scale remote sensing imagery is proposed. A robust Kalman filter- based algorithm is developed for the analysis of the dynamic behavior of hydrological indexes extracted from the real-world remotely sensed scenes. The simulation results verify the efficiency of the proposed approach as required for decision support in environmental resources management.Consejo Nacional de Ciencia y Tecnologí