Classification is an important process in interpretation of SAR images.In classification

the information

such as amplitude

phase and texture

is used to arrange all pixels in an image into different classes.A classification map shows directly classes of terrains

which is helpful to understand image.Classification methods of SAR images can be divided into supervised and unsupervised.Support vector machine（SVM） based on statistical learning theory

proposed by Vapnik et al.

is an effective supervised classifier.It is used widely in face recognition

hand-writing identification

and automatic target recognition for good classification performance with small training data sets.It has been a new focus in the field of machine learning.Several researchers have tried to use SVM for classifying polarimetric SAR images

and obtained promising results.As an advanced instrument for remote sensing

polarimetric synthetic aperture radar（SAR） has been applied widely in many fields

such as ecology

environmental monitoring

geological exploration

vegetation investigation

and so on.Compared with single-polarization SAR

to what extent dual-polarization and fully polarimetric SARs can improve in classifieation is important.Classification performance of full polarization versus dual and single polarization is compared qualitatively and quantitatively with SVM taken as the classifier in this paper.For fully polarimetric SAR data

six power values

extracted from the covariance matrix

and three eigenvalues

obtained by eigenvalue analysis technique using the coherency matrix

are contained in an input feature vector.For dual-polarization data

there are only three power values and two eigenvalues.And only one power value is used as an input feature for single-polarization data.In order to equilibrate effect of each element in an input feature vector on classification results

all features are normalized.According to the ground truth or a span image

training samples are selected to train SVM to obtain classifier parameters.Lastly

full-

dual-

and single-polarization SAR images are classified by the trained SVM

and the classification accuracy is calculated if the ground truth is available.In the first experiment

an L-band fully polarimetric image of Flevoland

Netherlands

acquired by the NASA/JPL AIRSAR sensor on August 16

1989

is used to analyze quantitatively the classification accuracy of full-

dual-

and singlepolarization SAR data.The results show that the classification accuracy of fully polarimetric SAR is highest

followed by dual-polarization SAR

and it is lowest for single-polarization SAR.For crop application

the accuracy of HH-VV SAR is greater than other two dual-polarization SARs

comparable with fully polarimetric SAR.If fully polarimetric SAR is unavailable

HH-VV SAR is a proper substitute with acceptable performance.Because of stronger depolarization capability

separability of each terrain in HV data is better than that in the other two cases.Consequently

classification accuracy of HV SAR is better than other two single-polarization SARs.For the two co-polarization SARs

performance of HH SAR is better than another.If the co-polarization transmitter and receiver are used

HH is more proper.In the second experiment

an HH-HV dual-polarization image

obtained in China

is used to analyze qualitatively classification performance of dual-and single-polarization SARs.The experimental results show that scattering power of building is badly confused with that of bank and bare soil due to weak depolarization of building.Thus the classification result of HV SAR is worse than HH SAR.Lastly

using detailed results of the above two experiments

classification performance difference of full-

dual-

and single-polarization SARs is explained from the point of view of scattering characteristics of terrains and operational mechanism of the classifier

SVM.