Polarimetric SAR data classification method using the self-organizing map

In this paper, we introduce a supervised classification method, which differentiates polarimetric SAR data into three categories using a self-organizing map (SOM) and a counter propagation learning approach after identifying the appropriate scattering classes. This classifier produces category maps corresponding to the Kohonen layers using training data for each scattering class. The SAR data are classified by inputting both like- and cross-polarization power elements into the learned SOM. In the experiment, PI-SAR data are employed since the resolution of aerial SAR data is higher than that of SAR data obtained from space. The proposed method yields higher-accuracy classifications than do conventional methods.