A Comprehensive Survey on SAR ATR in Deep-Learning Era

Abstract

Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.

Remote Sensing for Maritime Traffic Understanding

“…The accuracy of the centroid estimation process depends on the sampling resolution in the discrete frequency domain; hence, the acquisition of SAR imagery featuring finer resolutions (COSMO-SkyMed 2nd Generation, TERRA SAR X) could enhance the estimation performance. A potential solution to the lack of experimental data for validation purposes may be represented by methods, proposed in recent years (e.g., [72]), for the artificial generation of data. Such methods are mainly based on the usage of deep neural network architectures.…”

Zero-Shot SAR Target Recognition Based on a Conditional Generative Network with Category Features from Simulated Images

“…SAR target classification methods can be categorized into the traditional and the deep learning-based methods. The traditional method comprises the template matching method [3,4], the model-based method [5], and the machine learning-based method [6]. Among these, the machine learning-based method exhibits a fast processing speed, a high recognition capability and a robust performance.…”

CycleGAN-Based SAR-Optical Image Fusion for Target Recognition

“…The research of automatic target recognition (ATR) using SAR images has attracted considerable attention in recent years. Numerous attempts at progress have been made around the creation of SAR target datasets and the proposal of SAR ATR algorithms [1]. However, current SAR ATR technologies still have room for improvement in practical applications, which is mainly challenged by two aspects: the difficulty of SAR target feature extraction and the scarcity of training datasets.…”

“…However, current SAR ATR technologies still have room for improvement in practical applications, which is mainly challenged by two aspects: the difficulty of SAR target feature extraction and the scarcity of training datasets. (1) Due to the unique imaging mechanism of SAR, the target in SAR images exhibits distinct features that differ from its optical appearance. These characteristics, such as geometric distortion and speckle noise, seriously affect image quality.…”