Title :
Scattering power decomoosition using fully polarimetric information
Author :
Yamaguchi, Yoshio ; Singh, Gulab ; Park, Sang-Eun ; Yamada, Hiroyoshi
Author_Institution :
Dept. of Inf. Eng., Niigata Univ., Niigata, Japan
Abstract :
There exist 9 independent polarization parameters in the coherency or covariance matrix as the second order statistics. Various decomposition methods have been presented based on the physical scattering model using these parameters. However, none of them accounts for all polarimetric information, typically leaving T13 element un-accounted in the coherency matrix. This paper presents a complete four-component scattering power decomposition method using all polarimetric information. Using double unitary transformation of measured coherency matrix, it is possible to eliminate T23 element, which results in a reduction of polarization parameters from 9 to 7. Then by unitary transformation of expansion matrices, it becomes possible to account for T13 term, which has never been accounted for in the physical model-based decomposition. By the double unitary transformations to minimize the T33 component, all polarimetric parameters are accounted. This methodology also reduces the negative power problem significantly. This method is a further extension of the existing four-component decomposition. The four scattering powers (surface, double bounce, volume, helix) are assigned to blue, red, green, and yellow to compose full-color image. An example image of San Francisco area acquired with ALOS-PALSAR is shown to validate the decomposed result.
Keywords :
covariance matrices; geophysical image processing; radar imaging; radar polarimetry; statistics; synthetic aperture radar; ALOS-PALSAR; San Francisco; coherency matrix measurement; covariance matrix; double unitary transformation; expansion matrices; four-component scattering power decomposition method; full polarimetric information; full-color image; independent polarization parameters; physical scattering model; second order statistics; Covariance matrix; Mathematical model; Matrix decomposition; Remote sensing; Scattering; Synthetic aperture radar; Transforms; polarimetry; scattering power decomposition;
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.2012.6351629