Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.

Synthetic aperture radar (SAR) is often affected by other high-power electromagnetic devices during ground observation, which causes unintentional radio frequency interference (RFI) with the acquired echo, bringing adverse effects into data processing and image interpretation. When faced with the task of screening massive SAR data, there is an urgent need for the global perception and detection of interference. The existing RFI detection method usually only uses a single type of data for detection, ignoring the information association between the data at all levels of the real SAR product, resulting in some computational redundancy. Meanwhile, current deep learning-based algorithms are often unable to locate the range of RFI coverage in the azimuth direction. Therefore, a novel RFI processing framework from quick-looks to single-look complex (SLC) data and then to raw echo is proposed. We take the data of Sentinel-1 terrain observation with progressive scan (TOPS) mode as an example. By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. Then, through the analysis of the TOPSAR imaging principle, the position of the RFI in the SLC image is preliminarily confirmed. The possible distribution of the RFI in the corresponding raw echo is further inferred, which is one of the first attempts to use spaceborne SAR data to elucidate the RFI location mapping relationship between image data and raw echo. Compared with directly detecting all of the SLC data, the time for the proposed framework to determine the RFI distribution in the SLC data can be shortened by 53.526%. All the research in this paper is conducted on Sentinel-1 real data, which verify the feasibility and effectiveness of the proposed framework for radio frequency signals monitoring in advanced spaceborne SAR systems.

Radio Frequency Interference (RFI) significantly degrades the quality of spaceborne Synthetic Aperture Radar (SAR) images, and RFI source localization is a crucial component of SAR interference mitigation. Single-station, single-channel SAR, referred to as single-channel SAR, is the most common operational mode of spaceborne SAR. However, studies on RFI source localization for this system are limited, and the localization accuracy remains low. This paper presents a method for locating the ground-based RFI source using spaceborne single-channel SAR echo data. First, matched filtering is employed to estimate the range and azimuth times of the RFI pulse-by-pulse in the SAR echo domain. A non-convex localization model using Pulse Range Difference of Arrival (PRDOA) is established based on the SAR observation geometry. Then, by applying Weighted Least Squares and Semidefinite Relaxation, the localization model is transformed into a convex optimization problem, allowing for the solution of its global optimal solution to achieve RFI source localization. Furthermore, the error analysis on the PRDOA localization model is conducted and the Cramér–Rao Lower Bound is derived. Based on the simulation platform and the SAR level-0 raw data of Gaofen-3, we conduct several verification experiments, with the Pulse Time of Arrival localization selected for comparison. The results demonstrate that the proposed method achieves localization accuracy with a hundred-meter error in azimuth and a kilometer-level total error, with the total localization errors reduced to approximately 1/4 to 1/3 of those of the Pulse Time of Arrival method.

Accurate large-scale sea ice classification is vital for Arctic maritime activities. However, this task faces a fundamental challenge. Operational surveillance is restricted to wide-swath dual-polarization data, which limits classification precision due to polarimetric information deficiency. Conversely, while the quad-polarization mode offers the comprehensive scattering details required for more accurate classification, its narrow swath width prevents efficient large-scale monitoring. To address this challenge, we propose an enhanced sea ice classification method relying on limited co-region quad-polarization information to enhance dual-polarization data classification accuracy across larger spatiotemporal scales. Specifically, we construct a polarization-guided cross-mode knowledge distillation framework featuring an asymmetric teacher–student architecture. In this design, a hybrid CNN-Transformer teacher extracts robust scattering features from quad-polarization data to guide a lightweight student network operating on dual-polarization inputs. Through this transfer, the student acquires rich feature representations comparable to quad-polarization data, effectively compensating for the missing polarimetric scattering information. Experimental results on GF3-02 data demonstrate that the proposed method significantly outperforms the standalone dual-polarization network baseline, achieving an overall accuracy of 86.15%. This validates the effectiveness of the proposed method in enabling high-precision sea ice classification for large-scale monitoring.

The range-dependence on polarization distortion of spaceborne polarimetric synthetic aperture radar (SAR) affects the accuracy of wide-swath polarization applications, such as environmental monitoring, sea ice classification and ocean wave inversion. Traditional calibration methods, assessing the distortion mainly based on ground experiments, suffer from tedious active calibrator deployment work, which are time-consuming and cost-intensive. This paper proposes a novel polarimetric assessment and calibration method for the quad-polarized wide-swath ScanSAR imaging mode. Firstly, by using distributed target data that satisfy the system reciprocity requirement, we assess the polarization distortion matrices for a single beam in the mode. Secondly, we transfer the matrix results from one beam to another by analyzing data from the overlapping region between beams. Thirdly, we calibrate the quad-polarized data and achieve an overall assessment and calibration results. Compared to traditional calibration methods, the presented method focuses on using cross-beam (overlapping area) data to reduce the dependence on active calibrators and avoid conducting calibration work beam-by-beam. The assessment and calibration experiment is conducted on Gaofen-3 quad-polarized ScanSAR experiment mode data. The calibrated images and polarization decomposition results are compared with those from well-calibrated quad-polarized Stripmap mode data located in the same region. The results of the comparison revealed the effectiveness and accuracy of the proposed method.

Radio-frequency interference (RFI) severely degrades the imaging quality of synthetic aperture radar (SAR), especially when the interference energy is strongly coupled with ground backscatter in both the time and frequency domains. Existing algorithms typically rely on energy contrast or component decomposition in transform domains, which limits their ability to cleanly separate complex RFI from high-power echoes. Exploiting the fact that kurtosis is insensitive to ground clutter and background noise, this paper proposes an interference suppression network based on the temporal kurtosis guidance mechanism. Specifically, a statistical prior vector capturing the non-Gaussian characteristics of RFI is constructed using kurtosis in the time–frequency domain and is integrated into a multi-scale attention mechanism, allowing the network to more effectively concentrate on interfered regions. Meanwhile, a systematic framework is established for the quantitative assessment of phase fidelity in the reconstruction of complex-valued SAR echoes. On this basis, by exploiting the strong generalization capability and high processing efficiency of data-driven models, the proposed network achieves improved RFI separation and enhanced reconstruction accuracy of underlying scene features. Ablation experiments validated that the design of a kurtosis-guided module can reduce the mean square error (MSE) loss by 14.87% compared to the basic model. Furthermore, regarding the phase fidelity, the correlation coefficient between the suppressed signal and the original true signal reached 0.99. Finally, GF-3 satellite data are used to further demonstrate the effectiveness and practicality of the proposed method.