Aircraft target detection in synthetic aperture radar (SAR) images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution- or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a novel Diffusion-based aircraft target Detection network for SAR images (DiffDet4SAR). Specifically, the proposed DiffDet4SAR yields two main advantages for SAR aircraft target detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a denoising diffusion process of bounding boxes without heuristic anchor size selection, effectively enabling large variations in aircraft sizes to be accommodated and 2) the dedicatedly designed a scattering feature enhancement (SFE) module further reduces the clutter intensity and enhances the target saliency during inference. Extensive experimental results on the SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4% mAP50, outperforming the state-of-the-art methods by 6%. The code is available at https://github.com/JoyeZLearning/DiffDet4SAR .

The fundamental challenge in SAR target detection lies in developing discriminative, efficient, and robust representations of target characteristics within intricate non-cooperative environments. However, accurate target detection is impeded by factors including the sparse distribution and discrete features of the targets, as well as complex background interference. In this study, we propose a Gamma Diffusion Model Network with MambaSAR module (MaDiNet) for SAR target detection. Specifically, MaDiNet leverages the Gamma distribution to model the statistical characteristics of SAR images, and conceptulizes SAR target detection as the task of generating target bounding boxes in the image space. Furthermore, we design a MambaSAR module to capture intricate spatial structural information of targets and enhance the capability of the model to differentiate between targets and complex backgrounds. The experimental results on multi-class target detection datasets have all achieved SOTA, with a particularly notable improvement of 6.7% in mAP50 on the ODSOG-1.0 dataset, proving the effectiveness of the proposed network. Code is available at https://github.com/JoyeZLearning/MaDiNet.

The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR ATR research, there have been only a number of small datasets, mainly including targets like ships, airplanes, buildings, etc. There is only one vehicle dataset MSTAR collected in the 1990s, which has been a valuable source for SAR ATR. To fill this gap, this paper introduces a large-scale, new dataset named ATRNet-STAR with 40 different vehicle categories collected under various realistic imaging conditions and scenes. It marks a substantial advancement in dataset scale and diversity, comprising over 190,000 well-annotated samples—$10\times$ larger than its predecessor, the famous MSTAR. Building such a large dataset is a challenging task, and the data collection scheme will be detailed. Second, we illustrate the value of ATRNet-STAR via extensively evaluating the performance of 15 representative methods with 7 different experimental settings on challenging classification and detection benchmarks derived from the dataset. Finally, based on our extensive experiments, we identify valuable insights for SAR ATR and discuss potential future research directions in this field. We hope that the scale, diversity, and benchmark of ATRNet-STAR can significantly facilitate the advancement of SAR ATR.

Yuanxin Liu, Fandong Meng, Zheng Lin, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

The existence of adversarial examples causes serious security risks when deep neural networks are applied to synthetic aperture radar (SAR) target detection. In SAR image processing, the added small disturbances can cause the model to output incorrect predictions. Due to the multipath effect in the propagation of detection signals, there are complex interactions between targets and their surroundings serving as supportive clues for target detection. The interactions are manifested as tight correlations between pixels and contextual information in the SAR image (where context refers to various relationships, e.g., target-to-target co-occurrence relationships). In this letter, we proposed a novel conditional random field-based adversarial attack (CRFA) method, which disturbs the intrinsic interactions between the target and its surroundings. To the best of our knowledge, we are the first to exploit the contextual information for attacking the SAR target detector. We formulate the attack as an optimization problem and design the context information loss to calculate the energy differences in local feature patterns before and after perturbation. By maximizing the energy differences, the context area information around the target is destroyed, and the detector outputs the candidate box with a slight shift, even ignoring the ground truth and missing targets. Extensive experimental results on the SAR Ship Detection dataset (SSDD) demonstrate that our proposed algorithm reduces mAP by 4.29% on existing object detection models, validating the effectiveness of the method.