Remote Sensing Imagery and Signature Fields Reconstruction via Aggregation of Robust Regularization With Neural Computing
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Fecha
2007
Autores
Shkvarko, Yuriy
Villalón-Turrubiates, Iván E.
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Editor
Springer
Resumen
Descripción
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.
Palabras clave
Remote Sensing, Fused Bayesian Regularization, Neural Networks
Citación
Yuriy Shkvarko, Iván Villalón-Turrubiates, “Remote Sensing Imagery and Signature Fields Reconstruction via Aggregation of Robust Regularization with Neural Computing”, en Advanced Concepts for Intelligent Vision Systems – Lecture Notes in Computer Science, J. Blanc-Talon et al., Ed. Alemania: Springer Berlin Heidelberg, 2007, pp. 865-876.