Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To alleviate these problems, we propose a lightweight detection model (LDM), mainly including a reuse block (RB) and an information correction block (ICB) based on the Yolov3 framework. The RB module helps the neural network extract rich aircraft features by aggregating multi-layer information. While the RB module brings more effective information, there is also redundant and useless information aggregated by the reuse block, which is harmful to detection precision. Therefore, to accurately extract more aircraft features, we propose an ICB module combining scattering mechanism characteristics by extracting the gray features and enhancing spatial information, which helps suppress interference in a complex environment and redundant information. Finally, we conducted a series of experiments on the SAR aircraft detection dataset (SAR-ADD). The average precision was 0.6954, which is superior to the precision values achieved by other methods. In addition, the average detection time of LDM was only 6.38 ms, making it much faster than other methods.

Recovering the 3D representation of anisotropic targets from synthetic aperture radar (SAR) images is an intractable difficulty in the field of SAR target image interpretation. Recently, deep learning-based approaches have made significant progress in classical 2D vision tasks such as SAR target recognition and detection. However, due to the lack of spatial and depth information in SAR images, as well as the more complex task modeling and data requirements for 3D vision tasks, it is difficult to apply deep learning-based approaches to 3D reconstruction from SAR images. To address these challenges, we reformulate the SAR images 3D reconstruction as a sequenceto-sequence prediction problem and propose an end-to-end deep learning framework based on a feature hybrid Transformer network, called SAR-3DTR. The proposed method first extracts visual features from SAR image sequences. In addition, considering the unique scattering imaging properties of SAR images, we also extract and construct Scattering Features where represent rich physical structure information of target by introducing a novel Vector of Electromagnetic Scattering Features (VESF) module. Finally, transformer network learns 3D features and generate model prediction by exploring global correlations in visual, scattering and 3D spatial feature domains. Experiments conducted on the synthesized aircraft target dataset and the MSTAR ground target dataset show that our method has superior quantitative and qualitative results and reaches state-of-the-art performance on SAR 3D reconstruction.

With the development of artificial intelligence and synthetic aperture radar (SAR) imaging technology, methods based on deep learning have been gradually applied to SAR image aircraft detection. However, due to the special SAR imaging mechanism, the imaging results of aircraft targets in SAR images do not have clear features and appear as sparse scattering centers. In addition, airports contain strongly scattering buildings and objects, such as corridor bridges, which will bring huge interference to SAR aircraft target detection. To alleviate these problems, we propose an aircraft scattering information extraction and detection model (SIEDM) based on the spatial imaging characteristics of the targets. First, a scattering center feature extraction module is designed to extract the potential scattering centers of the aircraft targets in the SAR images and acquire the overall structural features of the aircraft targets. In addition, the adaptive noise suppression module is proposed to suppress background interference by learning global information. Finally, we conduct a series of experiments on an SAR image aircraft detection dataset. The experimental results show that the mean average precision of the aircraft SIEDM is 95.7%, which is better than other methods, and the proposed modules in this paper are effective.

Synthetic aperture radar (SAR) aircraft detection methods based on deep learning have become a current research hotspot. However, considerable challenges still remain due to the scattering feature of aircraft, variations in aircraft size, and interference from complex scenarios. To tackle these problems, the scattering feature extraction and fuse network (SFEF-Net) is proposed. First, considering the scattering characteristics of aircraft, we propose a scattering feature extraction and relation enhancement (SFERE) backbone based on the deformable convolution and the global context block. The SFERE backbone is used to extract the scattering feature of aircraft and model the correlation of scattering points. Furthermore, to enhance the detection ability for multi-scale aircraft targets in complex scenes, we redesign an attention bidirectional feature fusion pyramid (ABFFP). Two novel modules are proposed in ABFFP, namely, the attention guidance feature fusion (AGFF) module and the residual efficient channel attention (RECA) module. The AGFF module is proposed to suppress the interference of backgrounds and aggregate the multi-level feature maps. After the feature fusion operation, the output feature maps contain richer channel information, but there is some redundant information that could reduce the accuracy. Therefore, we adopt the RECA module to further select useful information in the channel dimension. To demonstrate the effectiveness of SFEF-Net, SAR aircraft images from the Gaofen-3 system are utilized in the experiments. The detection results show that the proposed model achieves competitive performance with an average precision of 95.5%.

Radar High Resolution Range Profile (HRRP), which can provide target structure information with great potential for target recognition. However, the structural information is not fully exploited by most existing deep learning methods, which focus only on local or sequence information. Furthermore, existing methods equalise target and non-target regions in HRRP. This is not conducive to target feature extraction. In this letter, we propose a target recognition method using wavelet patch merging and contraction Transformer, called CT. CT can adaptively focus on the target region and efficiently extract local and sequence information. CT used convolution to extract local features and contraction self-attention to extract sequential features. Wavelet patch merging was used to avoid oversampling. Finally, the experimental results show that the CT can effectively extract structural features in HRRP to improve target recognition performance. It is also robust under low signal-to-noise conditions.