SAR Target Recognition Research Articles

Physics-guided interpretable CNN for SAR target recognition

Deep Learning (DL) model has been widely used in the field of Synthetic Aperture Radar Automatic Target Recognition (SAR-ATR) and has achieved excellent performance. However, the black-box nature of DL models has been the focus of criticism, especially in the application of SAR-ATR, which is closely associated with the national defense and security domain. To address these issues, a new interpretable recognition model Physics-Guided BagNet (PGBN) is proposed in this article. The model adopts an interpretable convolutional neural network framework and uses time–frequency analysis to extract physical scattering features in SAR images. Based on the physical scattering features, an unsupervised segmentation method is proposed to distinguish targets from the background in SAR images. On the basis of the segmentation result, a structure is designed, which constrains the model’s spatial attention to focus more on the targets themselves rather than the background, thereby making the model’s decision-making more in line with physical principles. In contrast to previous interpretable research methods, this model combines interpretable structure with physical interpretability, further reducing the model’s risk of error recognition. Experiments on the MSTAR dataset verify that the PGBN model exhibits excellent interpretability and recognition performance, and comparative experiments with heatmaps indicate that the physical feature guidance module presented in this article can constrain the model to focus more on the target itself rather than the background.

Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition

Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition

Leveraging Visual Language Model and Generative Diffusion Model for Zero-Shot SAR Target Recognition

Simulated data play an important role in SAR target recognition, particularly under zero-shot learning (ZSL) conditions caused by the lack of training samples. The traditional SAR simulation method is based on manually constructing target 3D models for electromagnetic simulation, which is costly and limited by the target’s prior knowledge base. Also, the unavoidable discrepancy between simulated SAR and measured SAR makes the traditional simulation method more limited for target recognition. This paper proposes an innovative SAR simulation method based on a visual language model and generative diffusion model by extracting target semantic information from optical remote sensing images and transforming it into a 3D model for SAR simulation to address the challenge of SAR target recognition under ZSL conditions. Additionally, to reduce the domain shift between the simulated domain and the measured domain, we propose a domain adaptation method based on dynamic weight domain loss and classification loss. The effectiveness of semantic information-based 3D models has been validated on the MSTAR dataset and the feasibility of the proposed framework has been validated on the self-built civilian vehicle dataset. The experimental results demonstrate that the first proposed SAR simulation method based on a visual language model and generative diffusion model can effectively improve target recognition performance under ZSL conditions.

Complementary-View SAR Target Recognition Based on One-Shot Learning

The consistent speckle noise in SAR images easily interferes with the semantic information of the target. Additionally, the limited quantity of supervisory information available in one-shot learning leads to poor performance. To address the aforementioned issues, we creatively propose an SAR target recognition model based on one-shot learning. This model incorporates a background noise removal technique to eliminate the interference caused by consistent speckle noise in the image. Then, a global and local complementary strategy is employed to utilize the data’s inherent a priori information as a supplement to the supervisory information. The experimental results show that our approach achieves a recognition performance of 70.867% under the three-way one-shot condition, which attains a minimum improvement of 7.467% compared to five state-of-the-art one-shot learning methods. The ablation studies demonstrate the efficacy of each design introduced in our model.

Multiresolution SAR Target Recognition Based on Physical Attention Enhancement and Scale Distillation

Multiresolution SAR Target Recognition Based on Physical Attention Enhancement and Scale Distillation

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

SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition.

MIGA-Net: Multi-view Image Information Learning Based on Graph Attention Network for SAR Target Recognition

MIGA-Net: Multi-view Image Information Learning Based on Graph Attention Network for SAR Target Recognition

Feature Generation-Aided Zero-Shot Fast SAR Target Recognition With Semantic Attributes

Feature Generation-Aided Zero-Shot Fast SAR Target Recognition With Semantic Attributes

Convolutional Feature Aggregation Network With Self-Supervised Learning and Decision Fusion for SAR Target Recognition

Convolutional Feature Aggregation Network With Self-Supervised Learning and Decision Fusion for SAR Target Recognition

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition

Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition

Feature Joint Learning for SAR Target Recognition

Feature Joint Learning for SAR Target Recognition

A Few-Shot SAR Target Recognition Method by Unifying Local Classification With Feature Generation and Calibration

A Few-Shot SAR Target Recognition Method by Unifying Local Classification With Feature Generation and Calibration

A New Causal Inference Framework for SAR Target Recognition

A New Causal Inference Framework for SAR Target Recognition

Class Hierarchy Aware Contrastive Feature Learning for Multi-Granularity SAR Target Recognition

Class Hierarchy Aware Contrastive Feature Learning for Multi-Granularity SAR Target Recognition

A Novel Sequence Modeling Network for Multiview SAR Target Recognition

A Novel Sequence Modeling Network for Multiview SAR Target Recognition

SAR Target Recognition Based on Multi-view Differential Feature Fusion Network under Small Sample Conditions

SAR Target Recognition Based on Multi-view Differential Feature Fusion Network under Small Sample Conditions

Similarity to Availability: Synthetic Data Assisted SAR Target Recognition via Global Feature Compensation

Similarity to Availability: Synthetic Data Assisted SAR Target Recognition via Global Feature Compensation

An Azimuth Aware Deep Reinforcement Learning Framework for Active SAR Target Recognition

An Azimuth Aware Deep Reinforcement Learning Framework for Active SAR Target Recognition

Physics inspired hybrid attention for SAR target recognition

There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability. The attributed scattering center (ASC) parameters garnered the most interest, being considered as additional input data or features for fusion in most methods. However, the performance greatly depends on the ASC optimization result, and the fusion strategy is not adaptable to different types of physical information. Meanwhile, the current evaluation scheme is inadequate to assess the model’s robustness and generalizability. Thus, we propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the above issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target, so as to re-weight the feature importance based on knowledge prior. It is flexible and generally applicable to various physical models, and can be integrated into arbitrary DNNs without modifying the original architecture. The experiments involve a rigorous assessment using the proposed OFA, which entails training and validating a model on either sufficient or limited data and evaluating on multiple test sets with different data distributions. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters. Moreover, we analyze the working mechanism of PIHA and evaluate various PIHA enabled DNNs. The experiments also show PIHA is effective for different physical information. The source code together with the adopted physical information is available at https://github.com/XAI4SAR/PIHA.

Feature extraction analysis method of pre-trained CNN model for SAR target recognition

ABSTRACT Benefited from the latest advances in deep learning, convolutional neural network (CNN)-based SAR target recognition has made an excellent breakthrough. However, the most previous works pay attention to improve accuracy but neglect the working process analysis of the CNN models. Here, feature extraction of CNN plays an important role in recognition task. Moreover, in image processing field, frequency analysis is a classical way, which can directly reflect the frequency components of images. In view of that, we provide a feature extraction analysis method of pre-trained CNN in frequency domain in this paper. There are two analytical perspectives, i.e. amplitude and phase spectrum. Firstly, we can observe direction and range of pass-frequency through amplitude spectrum of convolution kernels, which represents information of raw SAR image and feature map captured from the convolution layer. Secondly, phase spectrums of SAR images store contour information of targets. We can understand the role of contour information from raw SAR images and feature maps in recognition task by phase spectrum analysis. In experiment part, according to the proposed feature extraction analysis method, we discuss the working process of several pre-trained CNN models. To some extent, it explains why the joint CNN has strong robustness against speckle noise in SAR image target recognition task. In short, the proposed feature extraction analysis method can improve the transparency and credibility of pre-trained CNN used for SAR target recognition.