Comparative study of some algorithms for terrain classification using SAR images
This paper describes the use of polarimetric SAR data for Earth terrain identification and classification. The K-distribution is used to describe the spatial characteristics of a forest area. Statistical methods using supervised and unsupervised techniques are also applied to segment the simple and the polarimetric data. The supervised classification is based on the ML algorithm assuming a Rayleigh distribution for the intensity pixels of the HH image and a circular Gaussian for the full polarimetric data. For the unsupervised classification, we use both the clustering and the ICM (iterative conditional modes) algorithms. Using such techniques, the segmentation we could classify areas of trees which differ by their age and which have in general an approachable scattering behaviour.<<ETX>>