This paper presents a new dual-channel convolutional neural network (Dc-CNN) for Polarimetric synthetic aperture radar (PolSAR) image classification when labeled samples are small. First, a neighborhood minimum spanning tree (MST) is used to enlarge the labeled sample set. Then, in order to obtain the abundant spatial information, a new dual-channel CNN is designed to PolSAR image to acquire different spatial features. This network model contains two parallel CNN structures, which can extract different features used two multiscale convolution structure. Experiments results show that compared with other methods, the proposed method shows a satisfactory classification result.