Unsupervised Land Classification by Self-organizing Map Utilizing the Ensemble Variance Information in Satellite-Borne Polarimetric Synthetic Aperture Radar

Polarimetric satellite-borne synthetic aperture radar is expected to provide land usage information globally and precisely. In this paper, we propose a two-stage unsupervised-learning land state classification system using a self-organizing map (SOM) based on the ensemble variance. We find that the Poincare sphere parameters representing the polarization state of scattered wave have specific features of the land state, in particular, in their dispersion (or ensemble variance). We present two-stage clustering procedure to utilize the dispersion features of the clusters as well as the mean values. Experiments demonstrate its high capability of self-organizing and discovering classification based on the polarimetric scattering features representing the land states.