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.

Increasing application demands are driving the need for future spaceborne synthetic aperture radar (SAR) systems with high resolution and continuous ultrawide swath capabilities. Azimuth multichannel staggered SAR, which integrates variable pulse repetition interval (PRI) and multichannel techniques, presents a promising solution. However, the resulting nonuniform sampling invalidates conventional frequency-domain reconstruction algorithms and increases signal processing complexity. To address this challenge, this paper proposes a uniform reconstruction scheme based on phase center adjustment (PCA). By introducing a phase center variation, the scheme compensates for nonuniform components to achieve equivalent uniform sampling during data acquisition. The PRI design criterion is established to minimize the maximum PCA value and provide the allowable range of the initial PRI. Furthermore, activation strategies for both transmit and receive antenna elements are defined to jointly achieve the required PCA. Simulation results validate the effectiveness of the proposed scheme.

Synthetic Aperture Radar (SAR) is easily affected by various radio frequency interference (RFI), which leads to the degradation of image quality. In multi-channel SAR, the position of the RFI source is a prerequisite for most spatial filtering methods to remove interference. For dual-channel SAR systems, the existing RFI source localization methods can only obtain the position of a single jammer, while in a complex electromagnetic environment, there may be multiple RFI sources. To address this problem, a multisource RFI localization method for dual-channel SAR is proposed in this letter. First, the time-frequency analysis is employed to establish the feature mapping of RFI and SAR signals in the time-frequency domain. Then, ridge path detection and recombination algorithms are proposed to separate and extract the received interference signals. Finally, the leastL1-norm models are established for each extracted interference signal based on the phase difference of the two channels, and the high precision azimuth position of the RFI sources can be obtained using the interior point method. Simulations indicate that the azimuth localization accuracy of the proposed method can be within 20 m when the Signal-to-Interference Ratio (SIR) is below 0dB. The spaceborne SAR data acquired by Chinese Gaofen-3 (GF-3) also verify the effectiveness of the proposed method.

Benefiting from the rapid development of deep learning, the field of synthetic aperture radar (SAR) ship detection has been promoted with renewed vigor. However, due to the unique characteristics of SAR ship detection, several challenges have been encountered in the process of fusing SAR ship detection. On the one hand, some special properties of SAR images, including low resolution, small targets, dense ship inshore arrangements, and background clutter noise, increase the difficulty of detection; on the other hand, satellite detection requires a high degree of real-time and lightweight, and most traditional models fail to satisfy the requirements in two areas. After considering the above issues, we combine the advantages of space-to-depth convolution (SPDConv) and inception depthwise convolution (InceptionDWConv) to design SPD-InceptionDWConv (SIDConv), which resolves the above problems with fewer parameters and operations. Moreover, based on SIDConv, we also design light SIDConv (LSIDConv) and efficient SIDConv (ESIDConv) to further reduce the model complexity and to accelerate the detection process. With the above module as the core and YOLOv5 as the base, we propose a lighter and faster detector named LFer-Net, which has only 0.8M parameters and 1.9G FLOPs. Extensive experimental results on the SSDD and HRSID datasets confirm that our model is not only superior to other models with 98.2 and 90.6 accuracy but also achieves detection speeds of 144 and 96 frames per second (FPS), respectively.

The electromagnetic environment is becoming complex as the usable spectrum will be allocated for more services. As a result of this situation synthetic aperture radar (SAR) missions are frequently perturbed by radio frequency interference (RFI) that jeopardizes their scientific observations all over the world. The state-of-the-art multichannel SAR has anti-RFI capability since it’s capable of digitally modulating the antenna pattern (AP) in postprocessing, thereby steering the null toward the angle of arrival (AOA) of the RFI in the spatial domain. However, the AOA of RFI is space-variant, meaning that the mitigation performance of beamformers sensitive to AOA will greatly deteriorate. In addition, the AOA of in-swath RFI and the target echo arrive simultaneously, thus the traditional beamformer will generate a distorted AP, deteriorating the SAR imagery. In light of these considerations, this article studies the in-swath and out-of-swath RFIs in elevation multichannel SAR and develops their countermeasures. Thereinto, a least$\ell _{1}$-norm model is developed to estimate the AOA of the RFI, followed by two schemes developed to separate the RFI. The former develops a beamformer that joint sidelobe control and null expanding to mitigate the space-variant out-of-swaths RFI, whereas the latter develops a blind source separation (BSS)-based technology to mitigate in-swath RFI, avoiding AP distortion and restoring SAR imagery. The effectiveness of the proposed approaches is supported by experiments based on the measured X-band airborne DBF-SAR data as well as the simulated SAR data.

Elevation multichannel synthetic aperture radar (SAR) utilizes digital beamforming (DBF) technology to achieve high-resolution and wide-swath (HRWS) imaging. However, electromagnetic jamming sometimes occurs in SAR images, resulting in the loss of scene information. A DBF-SAR system is weak in resisting jamming signals, and the barrage jamming signals within an imaging swath are not easy to remove using traditional suppression methods for DBF-SAR. Thus, an advanced barrage jamming suppression scheme based on elevation multichannel cancellation is proposed. First, the jamming signals in the echoes are removed using a well-designed channel cancellation filter to obtain the jamming-free signals. In accordance with the characteristics of jamming-free signals, the weighting coefficients are rededuced, and a modified DBF method is presented to achieve HRWS imaging and improve the DBF-SAR signal-to-noise ratio (SNR). In this way, the proposed scheme can effectively eliminate barrage jamming signals. In addition, some simulations and a 16-channel airborne DBF-SAR experiment were executed to demonstrate the proposed scheme.

Synthetic aperture radar (SAR), an active remote sensing equipment, shares the spectrum with devices in the same frequency band and is therefore easy to affect by pulse radio frequency interference (PRFI). The wideband version of PRFI, i.e., linear-frequency-modulation PRFI (LFM-PRFI), derived from the ground- and space-based radar sensors is a challenging issue for SAR because it usually has a large bandwidth and pulse width compared with the traditional PRFI. The well-known notch filtering methods, including time- and frequency-domain versions, are robust approaches against PRFI, which has been integrated into some SAR ground processing systems. However, the down-range frequency domain notch filtering will increase the sidelobes of the targets when mitigating wideband LFM-PRFI, whereas the time-domain version will introduce ghosts when notching large pulse-width ones. This paper proposed a filtering method that consists of three steps to tackle the above problems. The first step is focusing the energy of LFM-PRFI down-range and cross-range simultaneously, which can be done by match filtering and Fourier transform. The second step is mitigating the LFM-PRFI, which can be done by a designed filter. The last step is data restoration, which can be done by multiplying the conjugate of the phase function used before. Numerical experiments based on simulated SAR data and measured spaceborne SAR data acquired by European Sentinel-1 are performed to test the mitigation performance, which verified the effectiveness and superiority of the proposed approach.

Synthetic aperture radar (SAR) often shares spectrum with other systems, such as radio, TV, cellular network, and so on, which are likely to produce radio frequency interference (RFI). Pulse RFI, which hinders SAR signal processing and image interpretation severely, is a common form of RFI and cannot be neglected. The simple and easy-to-implement frequency-domain notch filtering (FNF) method has been widely used to mitigate narrowband pulse RFIs. However, a well-known problem with abnormal sidelobe effect, which is caused by missing spectrum gaps due to the notch operation, is aroused when using FNF. In this letter, a novel time-domain notch filtering (TNF) is proposed. In the proposed method, pulse RFI occurrences are detected and notched by a simple log-ratio operator in a pulse by pulse manner. Then, missing-data iterative adaptive approach (MIAA) is performed to recover the notched signal to avoid ghosts. Experimental results via simulated and real L-band airborne SAR raw data validate the performance of the proposed method.

In complex electromagnetic environments, synthetic aperture radar (SAR) is severely affected by radio frequency interference (RFI) from other systems, such as ground-based radar, cellular networks, and global positioning systems, and this interference cannot be neglected. Pulse RFI (PRFI), a common form of RFI, can hinder SAR signal processing and image interpretation to varying degrees. The time-domain notch filtering method designed for mitigating PRFI can locate and mitigate the evident PRFI covered in SAR echo data, but it is helpless against PRFI hidden in a strong echo signal. In this article, a three-step approach is proposed to tackle the PRFI problem. In the proposed approach, the first step is to detect and locate PRFI; this is based on eigenvalue decomposition (EVD) and the short-time Fourier transform (STFT). The second step is to notch PRFI, and this is based on a time-domain notch filter. The third step is to recover the notched signal using a novel matrix completion strategy, which integrates with a robust low-rank matrix completion (LRMC) technique—i.e.,the singular value thresholding (SVT) algorithm—and a well-known Lagrange interpolation technique. Experimental results via simulated SAR data, Sentinel-1 level-0 raw data, and L-band airborne SAR raw data demonstrate the performance of the proposed approach.

In synthetic aperture radar (SAR) data processing, radio frequency interference (RFI) has been recognized as a challenging issue, which can significantly degrade the image quality, including amplitude, phase, geometry, and so on. Most of the RFI sources are direct waves transmitted from the ground. Recently, a new form of RFI, namely, the mutual RFI (MRFI), which is a kind of scatter-wave RFI originating from the nearby ground area simultaneously illuminated by different SAR satellites, has been reported. In this article, the signatures of MRFI are characterized, and an example case study between Chinese GaoFen-3 (GF-3) and European Sentinel-1A (S-1A) is analyzed. In order to remove the artifacts caused by MRFI on SAR images, a novel RFI detector based on spectrum energy cancellation (SEC), which has the capability of detecting MRFI, is developed. Two methods, i.e., the traditional notch filtering method and an improved eigen-subspace projection (ESP) method proposed in this article, are employed to mitigate MRFI. The former method has robust mitigation performance with a fast processing speed, whereas the latter has an improved mitigation accuracy and lower sidelobes for strong scatterers. The methods are designed for cases in which the MRFI has a different radar center frequency than the useful signal, and some of the data are free from MRFI. The conditions required for the proper use of the approach are discussed. In contrast to the conventional mitigating methods performed on the raw data domain, the proposed approach begins with the focused single-look complex (SLC) SAR data. The effectiveness of the proposed approach is demonstrated on several GF-3 SLC SAR images, which were contaminated by MRFI from the S-1A.

Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This paper proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing images. 3) According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method.

Synthetic aperture radar (SAR), an active microwave remote sensing equipment, can provide all-day, all-weather, and high-resolution images for Earth observation. Ship monitoring using SAR is significant to fishery application, marine transportation, maritime security, and so on. Most traditional SAR ship detection methods use the amplitude information of the SAR imagery and employ the constant false alarm rate (CFAR), deep learning, and other technologies to detect the ship targets. However, in addition to the amplitude image, the SAR single-look complex (SLC) data can also be utilized for ship detection since its complex-valued information is beneficial to characterize the ship’s feature in the transforming domains. In this article, the strong scattering feature of ships in the image domain and low-rank feature in the transforming domain is studied, and on this basis, a two-step detector is proposed to detect ship targets in SAR SLC image. The first step is to identify the presence of ships, which is based on the eigen-subspace projection (ESP) technology to detect the occurrences of the ship targets in azimuth cells of the SAR data. The second step is to locate the position of ships, which is based on the low-rank and sparse matrix decomposition (LRSMD) to obtain the instantaneous frequency information of the ship targets in the time–frequency domain. Finally, the ship targets can be located in a line-by-line manner in the azimuth direction. Experimental results via European Sentinel-1A and Chinese Gaofen-3 single-pol SAR SLC data demonstrate the performance of the proposed detector.