Title :
A Bayesian reconstruction algorithm for synthesis aperture imaging radiometer
Author :
Yang, Hong ; Hu, Fei ; Chen, Ke ; Jin, Rong ; Sun, Jinhai
Author_Institution :
Dept. of Electron. & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
Regularization methods are very efficient in reconstructing the radiometric brightness temperature maps form interferometric measurements. The performance of these methods depends on selecting an optimal regularization parameter, which is typically defined by using the cross-validation or the L-curve. In this paper, we present a novel brightness temperature reconstruction method, which can automatically learn the optimal parameter by Bayesian learning without reducing the performance of the brightness temperature image. To support the theory, numerical simulations are presented and analyzed with emphasis on stability and error analysis.
Keywords :
Bayes methods; geophysical image processing; geophysical techniques; image reconstruction; radiometers; Bayesian reconstruction algorithm; L-curve; brightness temperature image; brightness temperature reconstruction method; error analysis; interferometric measurements; numerical simulations; optimal regularization parameter; radiometric brightness temperature maps; stability analysis; synthesis aperture imaging radiometer; Bayesian methods; Brightness temperature; Image reconstruction; Inverse problems; Noise; Radiometry; Reconstruction algorithms; Bayesian learning; brightness temperature reconstruction; synthetic aperture interferometric radiometer;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location :
Munich
Print_ISBN :
978-1-4673-1160-1
Electronic_ISBN :
2153-6996
DOI :
10.1109/IGARSS.2012.6351071