SAR Unlabeled Target Recognition Based on Updating CNN With Assistant Decision

Compared with optical images, the number of synthetic aperture radar (SAR) images is limited and growing slowly over time, and most of time the additional SAR images are unlabeled, which restricts the development of target recognition on SAR images. In this letter, the update learning method for SAR unlabeled target recognition based on the convolutional neural network (CNN) with assistant decision is proposed. The seed image set, which contains a small amount of existing labeled SAR images, is used for pretraining a CNN model. The unlabeled samples are fed to both CNN and the assistant classifier, and then, the matrices of the unlabeled samples based on recognition probability will be obtained, respectively. The unlabeled samples with high confidence by decision will be fed to CNN again to fine-tune the CNN model. The proposed method can make full use of the unlabeled samples to update the recognition model and to improve the recognition performance. Experiments on MSTAR data set show that, with the increase of unlabeled samples, the proposed method can improve the recognition performance and outperform the methods of CNN self-update learning and support vector machine update learning with the principal component analysis and the nonnegative matrix factorization. The experiments’ results demonstrate that the proposed method can effectively solve the problems caused by the lack of training samples.