The equivalent number of looks(ENL) is an important parameter in the multilook statistical model of polarimetric synthetic aperture radar(Pol-SAR). Recently, the maximum likelihood(ML) method was proposed and gave a good performance in the Gaussian model case by using the full covariance matrix instead of the intensity of Pol-SAR data, but it generated underestimates in the product model case. In this paper several novel ENL estimators are presented via certain cumulants of the log-determinant of the sub-matrices of the multilook polarimetric covariance matrix. The texture effect to the ENL estimates is eliminated, and the analytic estimators are derived. The estimators use the full covariance matrix and sub-matrices information, rather than the intensities of polarization channels. All the novel estimators are suitable for any texture model and thus provide more accurate results than many existing ones. Experiments using simulated data and real data are presented to evaluate the performance of different estimators. The results show that the second log-determinant moment(SLDM3)-based method is the best one among the novel estimators. At the same time this estimator has much less computational complexity. In addition, a novel distribution classification method is proposed by coloring the image via second- and third-order log-cumulants of the covariance matrix(MLC), which is helpful to assess the estimation result. The equivalent number of looks (ENL) is an important parameter in the multilook statistical model of polarimetric synthetic aperture radar (Pol-SAR). Recently, the maximum likelihood (ML) method was proposed and gave a good performance in the Gaussian model case by using the full covariance matrix instead of the intensity of Pol-SAR data, but it generated underestimates in the product model case. In this paper several novel ENL estimators are presented via certain cumulants of the log-determinant of the sub-matrices of the multilook The texture effect to the ENL estimates is eliminated, and the analytic estimators are derived. The estimators use the full covariance matrix and sub-matrices information, rather than the intensities of polarization channels. All the novel estimators are suitable for any texture model and thus provide more accurate results than many existing ones. Experiments using simulated data and real data are presented to evaluate the perfo rmance of different estimators. The results show that the second log-determinant moment (SLDM3) -based method is the best one among the novel estimators. At addition, a novel distribution classification method is proposed by coloring the image via second- and third-order log-cumulants of the covariance matrix (MLC), which is helpful to assess the estimation result.