Few-Shot SAR Target Recognition Based on Deep Kernel Learning

Abstract

Deep learning methods have achieved state-of-the-art performance on synthetic aperture radar (SAR) target recognition tasks in recent years. However, obtaining sufficient SAR images for training these deep learning methods is costly in time and labor. This paper focuses on recognizing targets with a few training samples, that is, few-shot target recognition. We combine deep neural networks' powerful feature representation capabilities with the nonparametric flexibility of Gaussian processes (GPs) and propose a fewshot recognition model based on deep kernel learning. Deep neural networks map input samples into a lowdimensional embedding space. GPs employ a family of kernel functions to measure the similarity between embedded samples and classify them. During training, the model builds diverse related tasks to learn kernel functions with parameters shared across few-shot tasks. These learned kernel functions define common prior knowledge that can be transferred to unseen tasks. During testing, the model can recognize novel tasks with a few samples based on learned kernel functions. We conducted extensive experiments on a widely-used real SAR dataset to evaluate the model's effectiveness. The test results demonstrate that our model is superior to several recently proposed few-shot recognition methods.INDEX TERMS Synthetic aperture radar (SAR), target recognition, deep kernel learning, few-shot classification

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

“…The target features were extracted by fine-tuning the pre-trained model of transferring VGG16 for the target recognition on the MSTAR dataset; the recognition accuracy was improved to 94.4%, which verified the feasibility of the application of transfer learning in SAR-ATR. Another method is to transfer the pretrained model learned from sufficient simulated SAR images to the real SAR images, which can effectively solve the problem of overfitting caused by insufficient SAR images [136].…”

“…Hence, simulation modeling is an alternative scheme to solve this problem, but how to ensure the fidelity of simulation data and obtain completely alternative data is an extremely challenging problem. In order to further advance the deep-learning-based RATR research with limited actual radar samples, some learning strategies should be emphasized, such as the following: (1) data augmentation, which has been adopted in [129][130][131]; (2) the GAN [132,133], which has been proven to be robust to the issue of insufficient training data; (3) various learning strategies such as transfer learning [134][135][136][137], metric learning [138][139][140], and meta learning [165,166] which can break through the limitation of data insufficiency; and (4) establishing more advanced learning-based methods for RATR which seems to be a solution [167].…”

FCCD-SAR: A Lightweight SAR ATR Algorithm Based on FasterNet

“…However, deeper CNNs focus on accuracy at the expense of real-time performance, speed, and compatibility with resource-constrained embedded platforms. Hence, there is a pressing need to develop lightweight models that strike a balance between speed and accuracy, enabling real-time ship target recognition in SAR images and seamless deployment on embedded platforms [ 15 ]. In the realm of traditional machine learning, feature extraction and classification algorithms are commonly used for target recognition.…”