ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 207-211, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
07 Jun 2016
J. X. Zhang, G. M. Huang, J. J. Wei, and Z. Zhao Institute of Photogrammetry and Remote Sensing, Chinese Academy of Surveying & Mapping, Beijing 100830, China
Keywords: Four-component decomposition, Generalized similarity parameter (GSP), Eigenvalue decomposition, Polarimetric SAR (PolSAR) Abstract. There are more unknowns than equations to solve for previous four-component decomposition methods. In this case, the nonnegative power of each scattering mechanism has to be determined with some assumptions and physical power constraints. This paper presents a new decomposition scheme, which models the measured matrix after polarimetric orientation angle (POA) compensation as a linear sum of five scattering mechanisms (i.e., odd-bounce scattering, double-bounce scattering, diffuse scattering, volume scattering, and helix scattering). And the volume scattering power is calculated by a slight modified NNED method, owing to this method considering the external volume scattering model from oblique dihedral structure. After the helix and volume scattering powers have been determined sequentially, the other three scattering powers are estimated by combining the generalized similarity parameter (GSP) and the eigenvalue decomposition. Among them, due to POA compensation, the diffuse scattering induced from a dihedral with a relative orientation of 45º has negligible scattering power. Thus, the new method can be reduced as four-component decomposition automatically. And then the ALOS-2 PolSAR data covering Guiyang City, Guizhou Province, China were used to evaluate the performance of the new method in comparison with some classical decomposition methods (i.e. Y4R, S4R and G4U).
Conference paper (PDF, 1277 KB)

Citation: Zhang, J. X., Huang, G. M., Wei, J. J., and Zhao, Z.: ALTERNATIVE TO FOUR-COMPONENT DECOMPOSITION FOR POLARIMETRIC SAR, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 207-211,, 2016.

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