基于特征空间结构对齐域自适应的机载SAR自动目标识别方法OA

Due to the rapid development of airborne Synthetic Aperture Radar(SAR)system and deep learning theory,the airborne SAR automatic target recognition technology based on convolutional neural network has made great progress.However,due to the small amount of SAR data obtained by real measurements,it is difficult to meet the requirement of deep learning algorithms for a large number of training samples.At present,there have been studies using simulated SAR images to make up for the defect of the small number of real SAR image samples.Due to the differences between simulated SAR images and real SAR images,the current mainstream research method is to map real images and simulated images into the same feature subspace through Domain Adaptation(DA),so as to extract domain invariant features.However,the current SAR ATR algorithm combining DA and simulation images only pays attention to the similarity of feature distribution of the single sample pair in different domains,but ignores that the feature distribution between sample pairs also contains a certain degree of semantic information.In order to solve the above problems,this paper proposes a DA algorithm based on feature space structure alignment to fully mine the semantic information shared between simulated SAR images and measured SAR images,thus significantly improving the recognition performance of deep learning models in the context of small samples.After sufficient experiment and analysis,the experimental results prove that our proposed method not only has high recognition accuracy,but also has strong universality and robustness.