The invention discloses a polarized SAR (synthetic aperture radar) image classification method based on a depth PCA (principal component analysis) network and an SVM (support vector machine) classifier. The polarized SAR image classification method includes filtering a polarized SAR image, extracting a shape feature parameter, a scattering feature parameter, a polarization feature parameter and independent elements of a covariance matrix C, and combing and normalizing into new high-dimensional features serving as data to be processed in a next step; according to actual ground feature flags, randomly selecting 10% of data with flags from each type to serve as training samples; whitening the training samples to serve as input to train a first layer of the network, taking a result as input of a second layer to train the second layer of the network, and performing binaryzation and histogram statistics on an output result; taking output of the depth PCA network as a finally learned feature training SVM classifier; whitening test samples, and inputting the test samples into a trained network framework to predict and calculate accuracy; coloring and displaying a classified image and outputting a final result.