Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique
Cargando...
Archivos
Fecha
2007
Autores
Shkvarko, Yuriy
Villalón-Turrubiates, Iván E.
Leyva-Montiel, José L.
Título de la revista
ISSN de la revista
Título del volumen
Editor
Institute of Electrical and Electronics Engineers
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. 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. We report some simulation results of hydrological RSS reconstruction from enhanced real-world environmental images indicative of the efficiency of the developed method.
Palabras clave
Signal Processing, System Fusion, Image Reconstruction, Regularization
Citación
Yuriy V. Shkvarko, Iván E. Villalón-Turrubiates, José L. Leyva-Montiel, “Remote Sensing Signature Fields Reconstruction via Robust Regularization of Bayesian Minimum Risk Technique”, in Proceedings of the 2nd IEEE International Workshop on Computational Advances in Multi-Sensor adaptive processing (CAMSAP), Islas Vírgenes EE.UU., 2007, pp. 237-240.