Deep learning-based methods hold significant potential for synthetic aperture radar (SAR) target detection, but they still face numerous challenges, including difficulty extracting global contextual features for large-scale targets, significant multi-scale issues, and the problem of feature extraction of SAR targets with large aspect ratios, which hinder further performance improvement. To this end, this paper proposes a WaveFormer module, which decomposes the image through wavelet convolution and uses convolution and Transformer to process the frequency domain components they are good at, respectively, to expand the receptive field with low parameter overhead and enhance the target feature extraction ability. To address cross-layer information attenuation during feature fusion, a Gather-and-Distribute(GD) mechanism is introduced to reconstruct the Neck network, enhancing multi-scale feature fusion and detection capabilities. Furthermore, given the large aspect ratio and distinct principal axis orientation of SAR targets, a Weighted Strip-Convolution(WSConv) is proposed to effectively improve detection performance. Experiments on the largest multi-class SAR target detection dataset, SARDet-100K, demonstrate that our method achieves a mean average precision (mAP) of 61.5%, reaching state-of-the-art performance and validating its effectiveness.

Synthetic aperture radar (SAR) ship detection faces significant challenges in nearshore scenarios due to strong backscatter interference from land and high ship density. Conventional detectors frequently fail in such environments when land mask information is unavailable. Ports, as hubs of maritime activity, have abundant and accessible SAR data archives, making them ideal testbeds for ship detection studies. Therefore, a two-step spatiotemporal fusion-based detector is proposed, which leverages the spatiotemporal characteristics of ships in SAR time series images. In the first stage, target localization is established through spatial spectrum analysis. In the second stage, difference image analysis is applied to the candidate regions to achieve precise spatiotemporal positioning, enabling effective detection without the need for land masks. An evaluation using a two-year dataset of 60 Sentinel-1A images from the Port of Santos confirmed the method’s effectiveness and superior performance.

The synthetic aperture radar (SAR) is a crucial tool for maritime observation, with ships being the main targets at sea. Detecting these ships is foundational for other downstream tasks, making the study of ship detection algorithms highly significant. Currently, ship detection primarily relies on deep learning algorithms, and training neural networks requires a large amount of data. In various maritime observation tasks, identifying defocused ships is particularly important. However, current databases lack defocused ship data, and SAR ship image generation algorithms are not yet mature. Therefore, this letter proposes generating defocused ship data using the existing ship data. First, we introduce the signal model of defocused ships and the causes of defocusing. Next, we describe the generation of defocused ships with nonuniform rotation by introducing motion errors through resampling. We also explain the generation of defocused ships with translational motion using initial phase compensation and envelope shift. Finally, expansion experiments using spaceborne SAR data demonstrate the effectiveness of our method.

Synthetic aperture radar (SAR), capable of ultra-wide swath imaging, serves as a vital tool for ocean surveillance. Ship detection in SAR imagery holds significant importance for maritime security and traffic management. Current ship detection algorithms exhibit excessive computational complexity, and both conventional and deep learning-based approaches struggle to achieve rapid detection in ultra-wide swath SAR imagery acquired by next-generation SAR systems. This paper proposes a rapid adaptive anchor framework for ship detection. First, the detection criterion statistics are decomposed into multiple prefix sum matrices, ensuring minimal computational complexity for anchor boxes of arbitrary sizes. Second, the image is quadrant-divided, with detection criterion statistics computed separately to determine ship presence in each quadrant. Third, quadrants containing ships undergo iterative quadrant division, while ship-free quadrants are annotated using differential matrices. Finally, the initial anchor boxes are systematically shifted to repeat the second and third steps across the entire image, ultimately generating a sea clutter mask. The computational complexity of the proposed ship detection framework remains equivalent to that of loading the full SAR image, introducing negligible additional overhead to existing systems. The framework is validated using empirical SAR datasets, demonstrating enhanced computational efficiency without compromising detection accuracy.

This letter proposes a scheme for automatically and accurately extracting the parameters of weak radio frequency interference (RFI) present in the signal within the time-frequency domain. This scheme utilizes Short-Time Fourier Transform (STFT) to transform each frame of radar echo containing interference into the time-frequency domain. In the time-frequency domain, a joint low-rank sparse robust principal component analysis based on truncated nuclear norms (TJLS-RPCA) is employed to divide the time-frequency representation into two joint low-rank sparse matrices, from which the interference parameters are extracted. Subsequently, K-means clustering is applied to determine the number of RFIs, and Gaussian fitting is applied to eliminate high-error items. This scheme can extract parameters such as the central frequency, bandwidth, and duration of the interference. The proposed scheme improves the accuracy of parameter extraction and processing speed, demonstrating notable effectiveness in handling weak interference. The Monte Carlo experimental results demonstrate that the scheme provides highly accurate parameter estimation when the Signal-to-Interference Ratio (SIR) is less than 13dB, with errors below 0.5%. Finally, the feasibility of the proposed scheme is validated using Sentinel-1A data, and the extracted results are consistent with the simulation results.

In the above article[1], there is a correction to the author list. The author list is as follows: