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In this paper, we propose a novel scheme of polarimetric synthetic aperture radar (PolSAR) image classification. We apply Laplacian eigenmaps (LE), a nonlinear dimensionality reduction (NDR) technique, to a high-dimensional polarimetric feature representation for PolSAR land-cover classification. A wide variety of polarimetric signatures are chosen to construct a high-dimensional polarimetric manifold which can be mapped into the most compact low-dimensional structure by manifold-based dimensionality reduction techniques. This NDR technique is employed to obtain a low-dimensional intrinsic feature vector by the LE algorithm, which is beneficial to PolSAR land-cover classification owing to its local preserving property. The effectiveness of our PolSAR land-cover classification scheme with LE intrinsic feature vector is demonstrated with the RadarSat-2 C-band PolSAR data set and the 38th Research Institute of China Electronics Technology Group Corporation X-band PolInSAR data set. The performance of our method is measured by the separability in the feature space and the accuracy of classification. Comparisons on the feature space show that the LE intrinsic feature vector is more separable than different original feature vectors. Our LE intrinsic feature vector also improves the classification accuracy.