The invention discloses a polarized SAR image classification method based on a dual-channel convolutional network. The method comprises the steps of filtering a to-be-classified polarized SAR image; extracting a multi-dimensional feature vector from a coherence matrix of each pixel point of the filtered polarimetric SAR image; performing spatial weighting on the polarized SAR image; randomly selecting a training sample and a test sample for each surface feature type of the polarized SAR data according to the real surface feature mark; constructing a multilayer convolutional network model; inputting the training sample into a multilayer convolutional network model to obtain a trained convolutional network model; inputting the test sample into the trained convolutional network model to obtain a classification result of each pixel in the test sample; comparing the classification result with a real surface feature mark, and calculating a correct rate; outputting the classification results.The method has higher classification accuracy on the ground objects, the homogeneous region is more complete, the region consistency and the classification performance are better, and the method is suitable for the ground object classification and target recognition on the polarized SAR images.