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).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.