In this paper, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in  and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reduced-size vectors, which feed a dominant eigenvalues classifier based upon the Model Order Selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization canbe representative of the type of structure which gives raise to the dominant backscattering mechanism.
- covariance matrix
- eigenvalues decomposition
- model order selection rules
- polarimetric SAR image classification
- structure classification