Enhanced Remote Sensing Imaging in Uncertain Environment Fusing Adaptive Least Squares and Variational Analysis Methods

Author

Vargas, José Tuxpan ; Arce, Stewart René Santos

fYear

2009

fDate

22-25 Sept. 2009

Firstpage

21

Lastpage

26

Abstract

This paper proposes a new approach for the reconstruction of the RS images degraded by composite noise (additive and multiplicative), taking into account the limitations of the sensor system. Our proposal takes advantage of the statistical and probabilistic qualities of the employed weighted constrained least squares (WCLS) and robust Bayes minimum risk (RBMR) algorithms. The restoration of the image optimized by aggregating the WCLS, RBMR, the maximum entropy and variational analysis techniques.

Keywords

Bayes methods; image enhancement; image reconstruction; least squares approximations; maximum entropy methods; remote sensing; variational techniques; RS image reconstruction; adaptive least square method; image restoration; maximum entropy technique; remote sensing imaging enhancement; robust Bayes minimum risk algorithm; variational analysis method; weighted constrained least squares; Additive noise; Degradation; Image analysis; Image reconstruction; Least squares methods; Noise robustness; Proposals; Remote sensing; Sensor systems; Working environment noise; descriptive regularization; fusion of methods; multi-sensor imaging; remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Robotics and Automotive Mechanics Conference, 2009. CERMA '09.

Conference_Location

Cuernavaca, Morelos

Print_ISBN

978-0-7695-3799-3

Type

conf

DOI

10.1109/CERMA.2009.21

Filename

5342017