Remote sensing imagery and signature fields reconstruction via aggregation of robust regularization with neural computing

Authors:
Yuriy Shkvarko;Ivan Villalon-Turrubiates
Affiliations:
CINVESTAV Jalisco, Avenida Científica, Zapopan Jalisco, México;CINVESTAV Jalisco, Avenida Científica, Zapopan Jalisco, México
Venue:
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Year:
2007

Citing 4
Cited 0

Image processing and data analysis: the multiscale approach

Image processing and data analysis: the multiscale approach
Adaptive image processing: a computational intelligence perspective

Adaptive image processing: a computational intelligence perspective
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Estimation of wavefield power distribution in the remotely sensed environment: Bayesian maximum entropy approach

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 developed method.