Classification of Full-polarimetric Synthetic Aperture Radar Data with Maximum a Posteriori

Considering the influence of the posterior and the statistic distributions of full-polarimetric SAR data,we proposed a new classification method of full polarimetric SAR data.First,the covariance matrix of polarization SAR data was converted to nine intensity quantities with normal distribution.Then,the probability of occurance for each class was calculated with iterative initial classification.Finally,the nine intensity images were classified with maximum likelihood classification method taking the probabilities of occurance for the classes into account.We applied the developed method to the ALOS PALSAR full-polarimetric data of Xunke County,Heilongjiang Province.The overall accuracy is 81.34% and the Kappa coefficient 0.84.The developed method showed higher accuracy than that from the traditional maximum likelihood classifier.This indicates that our method can improve the accuracy of classification.