The invention discloses a polarimetric SAR image classification method based on residual learning and a conditional GAN, and the method comprises the steps: (1), constructing a generator of the conditional GAN; (2), constructing a discriminator of the conditional GAN; (3), filtering a to-be-classified polarization SAR image; (4), performing pauli decomposition of a filtered scattering matrix; (5),normalizing a feature matrix; (6), generating a training data set and a test data set; (7), performing residual learning of deep and shallow features in the generator; (8), classifying features afterresidual learning; (9), obtaining a classification correctness rate; (10), training the generator of the conditional GAN; (11), classifying test data set. The method achieves the residual learning ofthe deep and shallow features of a polarimetric SAR image in the generator, achieves the extraction of the comprehensive feature information, achieves the good regional consistence of a classification result image, and is high in classification precision.