An SAR image-oriented small-sample semantic feature enhancement method, comprising the following steps: acquiring a sample set of an SAR target image, and training the sample set by transfer learning, so as to obtain an initialized deep neural network of the SAR target image (S100), so that the disadvantage that accurate feature values cannot be obtained by training due to an insufficient sample amount of the SAR target image is overcome, and the convergence speed of the deep neural network is effectively improved; using an activation function to perform network optimization on the deep neural network, and using the optimized deep neural network to perform feature extraction on the SAR target image, so as to obtain feature maps (S200), so that the feature values are more accurate; and using an autoencoder to map the feature maps between a feature space and a semantic space, so that the semantic space and feature space expression ability of an SAR image can be enhanced, and depth visual features having enhanced semantic features are obtained (S300), thereby improving the accuracy of target recognition in an SAR image.
SAR IMAGE-ORIENTED SMALL-SAMPLE SEMANTIC FEATURE ENHANCEMENT METHOD AND APPARATUS