An Unsupervised Classification for Fully Polarimetric SAR Data Using IHSL Transform and the FCM Agrithm

In this paper, the IHSL transform and the fuzzy C-means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric SAR data. We apply the IHSL colour transform to H/a/SPAN space to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/alpha/SPAN. Then the furry C-means algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/alpha/SPAN space directly during the segmentation procedure.