In this paper, expansion of feature vector using Rajski distance image is proposed to improve the accuracy of landcover classification using polarimetric SAR image data. To indicate a reason for the introduction of Rajski distance, simulation of Rajski distance value for the combination of fundamental scattering models is executed, and the results are inspected by comparison with calcurated value from some real polarimetric SAR data. To compare the classification accuracy for the different feature vector ; one of them is constructed only by amplitude images and the other is added Rajski distance images, the availability is shown by Euclid distance method and maximum likelihood method as supervised classifier. The expansion of feature vector proposed in this paper is able to be realized using amplitude images only since this method does not require any phase information.
Geometric distortions make the image analysis of monitored SAR data difficult. DEM (Digital Elevation Model) has been used to simulate SAR images to interpret the SAR images of hilly areas. In this study, we integrated geographic features to the DEM and make the simulated SAR image useful for both hilly and flat areas. Modification of DEM was done by overlaying a topographical map (1/25000) on the corresponding DEM, and then slightly modifying the altitude of the geographical features such as rivers. Those geographical features became visible in a SAR image simulated from the modified DEM and gave useful clues for interpreting observed SAR images. Such simulated SAR images are applicable for planning the strategy of SAR data analysis, training of SAR image interpretation, co-registration of SAR image and DEM in 2 pass Differential interferometry, and identification of ground control points.