Image classification performance depends on the understanding of image features and classifier selection. Owing to the special imaging mechanism, achieving precise classification for remote sensing imagery is still quite challenging. In this paper, a fully convolutional network with polarimetric manifold, is proposed for Synthetic Aperture Radar (SAR) image classification. First, the polarimetric features are extracted to describe the target information; then the feature points in high-dimension are mapped to low-dimension through the manifold structure. In this way, the effect of single manifold is equal to that of multi -layer convolution. The experimental results on SAR image data indicate that the presented manifold network can effectively separate the polarimetric features and improve the classification accuracy.