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.