DocumentCode :
3328740
Title :
Remote Sensing Signature Fields Reconstruction Via Robust Regularization of Bayesian Minimum Risk Technique
Author :
Shkvarko, Yuriy V. ; Villalon-Turrubiates, Ivan E. ; Leyva-Montiel, Jose L.
Author_Institution :
Center of Res. & Adv. Studies (CINVESTAV), Zapopan
fYear :
2007
fDate :
12-14 Dec. 2007
Firstpage :
237
Lastpage :
240
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. 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.
Keywords :
Bayes methods; geophysical signal processing; image reconstruction; radar imaging; remote sensing; synthetic aperture radar; Bayesian minimum risk; fields reconstruction; fused Bayesian-regularization; radar imaging; reconstructive imaging; remote sensing signature; scene analysis; sensor array radar; synthetic aperture radar; Bayesian methods; High-resolution imaging; Image analysis; Image reconstruction; Image sensors; Large-scale systems; Radar imaging; Remote sensing; Robustness; Sensor arrays; Signal processing; image reconstruction; regularization; system fusion;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop on
Conference_Location :
St. Thomas, VI
Print_ISBN :
978-1-4244-1713-1
Electronic_ISBN :
978-1-4244-1714-8
Type :
conf
DOI :
10.1109/CAMSAP.2007.4498009
Filename :
4498009
Link To Document :
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