Ref ined land-cover classif ication algorithm in airborne POLSAR system
机载极化SAR 地物类型逐步精细分类研究

In high resolution X-band POLSAR image, the water body, cement roads and the bare soil are always at low level radar backscattering signals, which is caused by no Bragg scattering phenomenon in smooth surfaces. The prevalent H/Alpha- Wishart and Freeman-Durden methods cannot distinguish those targets. This paper explores the improved X-band classif ication algorithm based on the pre-classif ication result for low backscattering objects in urban areas. The occurrence plane which is combined by entropy and the standard deviation of the co-pol channel diff phase is used to ref ine the pre-classif ication. The experiments show that the overall accuracy is above 80% and Kappa coeff icient is higher than 0.7. The improved method improves the potential to distinguish the mixture classes of the low backscattering objects.