Classification of multi-look Polarimetric SAR imagery and polarization channel optimization

摘要

In this paper, a new maximum likelihood(ML)classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar(SAR)imagery. Experimental results with the NASA/JPL airborne L-band polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classification with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.