A decomposition-free scattering mechanism classification method for PolSAR images with Neumann’s model

Abstract

This letter focuses on scattering mechanism classification of polarimetric synthetic aperture radar (PolSAR) images. Scattering mechanism classes are defined as the different combinations of dominant and secondary scattering mechanisms. By analysing the general characteristics of surface scattering, double-bounce scattering and volume scattering, we propose a maximum likelihood classifier to classify PolSAR pixels into nine classes with three PolSAR metrics. The probability density functions of various classes are obtained via extensive simulations. This method is not only a good classification method free of polarimetric decomposition but can also serve as a pre-classification step for advanced classification scheme as well. Furthermore, it is able to simplify incoherent polarimetric decomposition, so that we can employ incoherent scattering models for all components.

Acknowledgements

The authors thank anonymous reviewers for reviewing this letter.

Funding

This work was supported by National Key Fundamental Research Plan of China (973) [No. 2012CB719906].