Comparison of algorithms for the classification of polarimetric SAR data

Most of the current SAR systems aquire fully polarimetric data where the obtained scattering information can be represented by various coherent and incoherent parameters. In previous contributions we reviewed these parameters in terms of their “utility” for landcover classification, here, we investigate their impact on several classification algoritms. Three classifiers: the minimum-distance classifier, a multi-layer perceptron (MLP) and one based on logistic regression (LR) were applied on an L-Band scene acquired by the E-SAR sensor. MLP and LR were chosen because they are robust w.r.t. the data statistics. An interesting result is that MLP gives better results on the coherent parameters while LR gives better results on the incoherent parameters.