On ICA Based ICTD Classification of Polsar Data

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within PolSAR images. This paper addresses an important aspect for applying such methods on real data, namely statistical classification with ICA. A novel algorithm is proposed by adjusting the iterative segmentation from [1], [2] to the particular nature of the Touzi's polarimetric decomposition [3]. This algorithm is tested using P-band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.