Farmland segmentation is crucial for precision agriculture and land resource management, and very high resolution synthetic aperture radar (VHR SAR) offers valuable data support. Yet, effectively leveraging this data remains challenging. Convolutional neural networks (CNNs) struggle to capture long-range dependencies, while transformers, though effective in long-range modeling, suffer from high computational costs. Additionally, noise in VHR SAR images often results in ambiguous edge semantics. To address these issues, a farmland segmentation network for VHR SAR, named the CNN-VMamba network (CVMNet), is proposed in this letter. Specifically, a gradient-curvature joint algorithm (GCJA) is applied to VHR SAR images to generate edge strength map, providing enhanced contour information. In parallel, a CNN-based local feature extraction (LEF) block, sensitive to tubular structures, is employed to capture fine-grained spatial details. For efficient long-range modeling with linear computability, a 2-D selective scan (SS2D) mechanism from VMamba is integrated. Furthermore, an SS2D-based shallow-deep feature fusion module (SDFFM) is proposed to facilitate cross-layer feature fusion. Experiments using TerraSAR-X images showed that the proposed method outperforms compared methods, yielding an F1-score of 96.95% and an intersection over union (IoU) of 94.08%. The code is available at https://github.com/onesar/CVMNet.

The complex and diverse geological structures on the lunar surface serve as a direct record of its long evolutionary history. Comprehensively identifying and classifying these geological structures can not only deepen our understanding of lunar evolution, but also support the planning of future lunar exploration missions and guide the detection of lunar energy resources. However, the diverse and intricate morphologies of lunar geological structures, coupled with certain similarities between different formations, make their automatic identification and classification a significant challenge. To address this issue, we propose a multi-type lunar geological structure recognition network based on cross-view constraints, which mines differentiated feature information to enhance target identification and discrimination capabilities. This network effectively extracts heterogeneous and complementary features through cross-view feature extraction constraints. By employing a discrepancy-weighted loss function, the network focuses on regions where discrepancies arise in the recognition results across multiple views, thereby enhancing attention to divergent areas and learning feature representations in complex scenarios. Additionally, multi-scale contextual information aggregation combines contextual features from different receptive fields, leveraging surrounding terrain to enhance discriminative information for lunar geological structures. Experimental results demonstrate that the proposed method exhibits significant superiority in the task of identifying and classifying multi-type lunar geological structures, with a 1.9% improvement in mIoU.

The infinity and orthogonality of the orbital angular momentum (OAM) of vortex electromagnetic waves (VEMWs) provide a new degree of freedom for the modulation of information. The VEMW generated by the uniform circular array (UCA) exhibits the characteristics of the Bessel function in terms of amplitude, and there is a null amplitude region in the beam center. This characteristic is particularly obvious when the array radius is small. In practical applications, it is usually necessary to switch OAM mode to ensure the richness of the received information. This leads to a sudden drop in radiation energy when switching OAM modes, and the OAM mode cannot be fully utilized. To address this issue, this paper proposes an OAM mode switching method based on the characteristics of the intersection of the adjacent-order Bessel functions for radar forward-looking imaging. Meanwhile, this paper introduces OAM mode repetition frequency (OMRF) to indicate the “speed” of switching OAM mode. Finally, through the application in the field of forward-looking radar imaging, it is proved that the method proposed in this paper can effectively improve the OAM mode utilization rate and anti-noise ability.

Topography exerts a significant influence on the polarimetric parameters of synthetic aperture radar (SAR), making its study crucial for advancing scientific research. Accurate analysis of topography-independent content requires the removal of lunar topography effects. This letter presents a statistical-based method for assessing the impact of topography on SAR images. It involves the calculation of various SAR parameters and local incidence angle (LIA) using Lunar Reconnaissance Orbiter’s (LRO) Mini-RF SAR and Lunar Orbiter Laser Altimeter (LOLA) digital elevation model (DEM) data, respectively. By establishing a mapping relationship between LIA and SAR parameters through coregistration, the proposed method enables an insightful examination of topography effects. The method is applied to analyze and mitigate topography influences on lunar SAR images acquired by Mini-RF, indicating that LIA has a pronounced effect on SAR parameters in relatively flat areas, while its correlation weakens in rougher regions. This analysis provides valuable insights for classifying lunar regolith based on the effects of topography on SAR images.

Traditional jamming methods protect scene targets by generating barrage jamming or deceptive jamming effect, but the single jamming effect is currently difficult to meet the increasingly complex electromagnetic environment. To meet the complex electromagnetic environment, a 2-D precise controllable smart jamming method is proposed in this letter, which can produce two kinds of jamming effects: barrage jamming and deceptive jamming. Based on the synthetic aperture radar (SAR) imaging properties of linear frequency modulation (LFM), the range and azimuth modulation terms have been designed to generate precise controllable jamming. First, both the range and azimuth jamming positions can be controlled by first-order phase error modulation. Subsequently, multiple phases sectionalized modulation is performed in the range direction, quadratic phase error or periodic phase error modulation is performed in the azimuth direction. Different jamming effects can be generated by changing the jamming modulation item. Theoretical analysis and simulation results show that the proposed method can produce 2-D precise controllable smart jamming effects.

Lunar crater ejecta can provide important information regarding the impact cratering process and the properties of subsurface materials. However, the mapping of the range of the crater ejecta has been hampered by the degradation of the lunar surface’s morphological features. Polarimetric synthetic aperture radar (SAR) is an effective technique to determine the scattering characteristics of the lunar surface and subsurface, and for distinguishing the fresh crater ejecta from surrounding regions. This letter uses a three-component compact decomposition to process the Mini-RF data to identify the scattering characteristics of the crater floor, crater wall, crater ejecta of the simple fresh crater, and lunar background regolith. Then, a method for mapping the range of crater ejecta is proposed to obtain the boundary between the crater ejecta and other regions by analyzing the differences in polarimetric scattering characteristics. Using this method, the crater ejecta of six craters were extracted and verified with the range of the ejecta obtained through visual interpretation on SAR imagery, the accuracy of the ejecta range obtained by the proposed method is 82%-95%. The results show that the proposed method can effectively depict the range of the simple fresh lunar crater ejecta.

The high-resolution and wide-swath (HRWS) mode in azimuth multichannel systems has been widely used in current on-orbit and future spaceborne synthetic aperture radar (SAR) systems. It is crucial to explore jamming methods for these systems. This letter proposes a jamming method for HRWS SAR that can flexibly produce controllable barrage effects on multiple targets simultaneously. First, a position modulation function is designed based on a reconstruction method for the azimuth multichannel HRWS SAR system. This function modulates the intercepted signal, allowing for flexible control of periodic jamming positions. Next, a coverage modulation function is designed. The intercepted signal is then modulated by this coverage modulation function, enabling control over the coverage of noise-like jamming. Finally, the modulated signal is retransmitted by the jammer, creating a flexibly controllable periodic multitarget barrage jamming effect in the SAR image. In both point target and area target simulation experiments, quantitative comparisons of the position control factor and coverage control factor show that the experimental results align with theoretical analysis and deductions, verifying the effectiveness of the proposed method in the azimuth multichannel HRWS SAR system.

Dual-polarized inverse synthetic aperture radar (ISAR) systems can provide more extra information than single-polarized ISAR systems, which can be used to increase image quality during the imaging process. However, there is limited research on the subject of dual-polarized ISAR. In this letter, an enhanced phase gradient autofocus (PGA) method was proposed for dual-polarized ISAR imaging. The Cloude-Pottier decomposition is employed to extract the polarization parameters$H$and$\alpha $from the dual-polarized primary image. Subsequently, the PGA is enhanced by selecting high-quality prominent scatterers based on these$H$and$\alpha $parameters. By making full use of the prominent scatterers selected by polarization information, the performance of PGA was improved, and thus, the imaging quality was significantly improved. Finally, both the real measurement data of aircraft targets and ship targets verified the effectiveness of the proposed method.

In the data processing of spaceborne synthetic aperture radar (SAR), radio frequency interference (RFI) will seriously affect amplitude, phase, and other information, thereby deteriorating image quality. Mutual RFI (MRFI) between spaceborne SAR satellites is a typical terrain scattered wave interference, and it usually has long duration and large bandwidth, which brings huge challenges to interference mitigation. From the perspective of the European Sentinel-1A (S-1A), the ultrawideband MRFI characteristics from different SAR satellites in the C-band are studied and analyzed in this article. In order to mitigate the impact of ultrawideband MRFI on SAR data, an effective solution is proposed, including two steps. The first is a novel out-of-band spectrum energy (OSE) detection method, which has the ability to detect MRFI with high accuracy. Meanwhile, the second is an improved version of eigen-subspace projection (ESP), namely, 2-D dynamic threshold ESP (2DDT-ESP) method, which can better deal with complex spatial scene changes and obtain higher precision MRFI mitigation effects. The conditions for the proper use of the above methods are discussed in this article. Comparative experiments with other methods based on simulated SAR data and three sets of S-1A level-0 raw measured data are conducted, in which the experimental measured dataset is contaminated by MRFI from different sources, including Radarsat-2 (RS-2), Gaofen-3 (GF-3), and Radarsat Constellation Mission (RCM). The final processed results verify the effectiveness and superiority of the proposed approach.

Radio frequency interference (RFI) seriously affects the processing of synthetic aperture radar (SAR) data and the interpretation of images. The accuracy of RFI detection, which is the first step of RFI suppression, directly affects the follow-up work. However, the implementations of existing RFI detection methods mostly rely on the assumption that the flatness of the range spectrum will be destroyed by strong RFI, making these methods ineffective in the face of weak energy RFI. Furthermore, in these methods, the problem of accurately localizing the RFI is rarely considered, resulting in a considerable loss of imaging signals during subsequent RFI suppression. In this manuscript, a method of time-varying RFI detection and localization is proposed with two steps that are based on the flatness and symmetry of the SAR range spectrum. This method can effectively avoid missed detection and accurately find the location of RFI with weaker energy in an echo. The proposed method first detects the existence of RFI along the azimuth direction. Moreover, the proposed composite index is used. The position of the RFI is then found along the range direction, and the log ratio operator and eigenvalue decomposition (EVD) are used. Experimental results on both simulated and raw data obtained by airborne and spaceborne SAR validate the advantages of the suggested method in detecting and locating weak energy RFI.

The azimuth multichannel synthetic aperture radar (AMC-SAR) system makes possible simultaneous high-resolution and wide-swath (HRWS) Earth Observation. The motion of moving targets illuminated by the AMC-SAR system will not only lead to the failure of signal reconstruction, resulting in false targets in the image, but also to the defocusing of the moving targets. These problems can be effectively addressed by the estimation of the moving target parameters and their compensation in the signal. The existing imaging methods focus on eliminating the impact of radial velocity on signal reconstruction and disregard the influence of other motion parameters. However, the radial acceleration, azimuth acceleration and azimuth velocity of moving targets affect the accuracy of the signal reconstruction and focusing of the images. To eliminate the influence of target motion, this Letter proposes a moving target imaging method. First, the proposed method transforms the problem of parameter estimation into the problem of phase error estimation. Second, the quadratic phase error (QPE) is accurately estimated via autofocus theory and compensated in the signal, improving the focusing of a moving target. Then, the linear phase error (LPE) is accurately estimated via subspace theory and compensated in the signal, improving the accuracy of signal reconstruction. The effectiveness of our proposed method is demonstrated through processing of simulated SAR data.

Synthetic Aperture Radar (SAR) images of Sentinel-1 (S-1) in Interferometric Wide (IW) swath mode, which are widely used for land and ocean services, but are affected by severe radio frequency interference (RFI). Single Look Complex (SLC) data containing phase and amplitude information is the most popular product type for SAR applications. The sub-band spectral cancellation (SSC) method extracts and suppresses RFI in SLC images through making a subtraction among different sub-images, which is efficient and strong enough to be applied to projects based on the hypothesis that the range spectrum is particularly symmetric. However, through the theoretical analysis of the imaging algorithm for generating SLC data in S-1 IW mode, it is found that the phase compensation step in the azimuth post-processing destroys the symmetry of the spectrum and does not meet the basis for the application of the SSC method, resulting in poor RFI mitigation performance. Based on this discovery, inverse phase compensation is first performed on the RFI-contaminated SLC image to restore the spectrum symmetry. The second step is the sub-band division and sub-image energy cancellation. This method effectively protects useful signals while suppressing RFI, which is significantly essential for the further application of S-1 SLC images. The availability of the method has been proved by simulated and measured experiments.

In recent years, the application of deep learning for water-body detection in Synthetic Aperture Radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the Dung Beetle Optimizer-Convolutional Neural Network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs DBO algorithm to search for optimal hyperparameter of CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from H/α and model-based polarization decomposition methods with backscatter characteristic. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and Intersection over Union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provides new insights and in-depth understanding for detecting water-body.

Although the multiple-phase-sectionalized-modulation (MPSM) jamming can produce barrage jamming effects in a local range, the range of barrage jamming can't be controlled. Against this disadvantage, an improved MPSM jamming method based on a non-linear frequency-modulate (NLFM) signal, i.e., MBN, is proposed in this paper. The MBN realizes the control of the jamming range in the fast time domain based on the controllable time-frequency structure of the NLFM signal. To generate the desired NLFM signal, the shaping factor is defined. Subsequently, the feasibility and effectiveness of the MBN jamming method are confirmed by simulation experiments.

Soil moisture is an important parameter affecting environmental processes such as hydrology, ecology and climate. Microwave remote sensing is an effective means of surface soil moisture measurement. Aiming at the influence of vegetation cover in the process of surface soil moisture inversion of winter wheat farmland by microwave remote sensing, a cooperative inversion method using multi-source remote sensing data is proposed in this paper. Thirty-three feature parameters are extracted from Radarsat-2 full polarization SAR data and Sentinel-2 optical data, and ten parameters with high correlation with soil moisture are selected to participate in soil moisture inversion by Pearson correlation analysis. Combined with the ground sampling data, four machine learning models, including Random Forest, Generalized Regression Neural Network, Radial Basis Function and Extreme Learning Machine, are used for quantitative inversion of soil moisture to reduce the impact of vegetation and improve the inversion accuracy. The experimental results show that the Random Forest model is the optimal. The average of determination coefficient is 0.63959, and the average of root mean square error is 0.0317 cm3/ cm3, which provides a reference for the inversion of soil moisture in farmland using multi-source remote sensing data.

The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. As the origin of Chinese civilization, AYR provided natural conditions for the people to thrive. However, the sediment carried by AYR still has a negative impact on local agricultural production. This letter, based on Gaofen-3, extracted scattering characteristics of archeological anomalies, detecting part of AYR during Song and Jin Dynasties, which was confirmed with field investigations. Then, an adaptive irregular convolution kernel U-Net (AICK-U-Net) was proposed to reconstruct the channel of AYR, based on the images obtained by the different polarization decomposition methods in October, and the precision and recall reached 96.21% and 94.45%, respectively. Finally, two decision-level methods were proposed to optimize the reconstruction results, improving the precision and recall to 96.39% and 97.36%, respectively. In summary, Spaceborne synthetic aperture radar (SAR), with the application of polarization decomposition and neural network, provides new insights for detecting archeological anomalies and reconstructing archaeolandscapes.

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.

The equivalent rotation vector synthesized by 3-D nonuniform rotation of ship target leads to the time-varying characteristic of Doppler frequency of ship echo data, which will cause the image to be defocused in azimuth. In order to obtain high-resolution inverse synthetic aperture radar (ISAR) images of ship targets, we developed an optimal time selection algorithm based on time-frequency analysis of multiple scatterers. In this letter, we addressed the challenges of the serious cross-term interference in time-frequency analysis and measuring the stability of Doppler frequency. The time interval with a minimum variation of Doppler frequency was determined by using time-frequency analysis of multiple isolated scatterers and root mean squared error (RMSE). The effectiveness and robustness of the proposed algorithm were evaluated by both simulated and real ISAR data.

The concept of spaceborne interrupted frequency-modulated continuous-wave (IFMCW) synthetic aperture radar (SAR) subverts the design principles of the traditional frequency-modulated continuous-wave (FMCW) SAR system, which uses a single physical antenna to transmit signal and receive echo alternatively. Due to the demand of turning off the receiver in the process of transmitting signal, periodical data-missing phenomena will be aroused in the echo data, and the missing ratio is close to 50%. When traditional imaging algorithms were used to process the echo data, the artifacts, appearing as periodically replicated false targets, will be generated in the focused SAR image due to the periodical missing data, thus affecting the image interpretation. In order to effectively suppress the artifacts, a novel method exploiting singular value threshold-based matrix completion (SVT-MC) technology was proposed in this letter, where the singular value decomposition of the Hankel matrix was utilized to recover the missing data with high precision. Finally, the experimental results based on both point targets and distributed targets verify the effectiveness and superiority 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.

Synthetic aperture radar (SAR) is an advanced ground-observing remote sensing technology, and high-resolution wide-swath (HRWS) imaging has always been the goal of SAR. In general, an improved azimuth resolution requires a large pulse repetition frequency (PRF), resulting in high system requirements. To overcome this difficulty, azimuth multichannel technology has gradually developed, achieving HRWS imaging and decreasing the PRF by increasing the spatial sampling, i.e., increasing the number of antenna subapertures in the azimuth. This article proposes a theoretical architecture that generates multiple virtual receiving apertures in the azimuth rather than real apertures. The virtual receiving apertures are formed by multiplying the azimuth signals by linear phase histories provided by vortex beams carrying different orbital angular momentum (OAM) modes. Vortex beams with different OAM modes have different oblique phase wavefronts, so virtual receiving positions are generated in the along-track direction. This approach aims to reduce the PRF without increasing the azimuth real receiving aperture. Simulation results demonstrate the effectiveness and limitations of the method. Finally, to overcome the inherent limitations of the method, two possible implementation schemes are proposed.

In order to realize the raw data simulation of high resolution squint spotlight Synthetic Aperture Radar (SAR) efficiently, an effective acceleration algorithm is proposed. This algorithm combines the time-domain raw data simulation model and its signal characteristics to compensate the range cell migration (RCM) existing in the raw data simulation process of squint spotlight SAR. An adaptive data partitioning algorithm is used, and computes partitioned data respectively in GPU. Then the partitioned data are transmitted and spliced. The algorithm improves the computational efficiency of time-domain raw data simulation, and it solves the problems of huge volume of raw data, limitation of GPU memory and data transmission. The experimental results of point target and distributed target show that the speedup ratio of this algorithm reaches 209.93, which verifies the effectiveness of the proposed method.

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible.

Highly squint synthetic aperture radar (SAR) increases the flexibility and aspect-information of observation but poses several challenges to frequency-domain algorithms. The back-projection (BP) algorithm is recognized to produce the best results for highly squint mode but at high computational expense. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm has been proposed to improve the performance. However, CFBP is ineffective in the highly squint case because its range model (RM) is imprecise. In this letter, we propose a modified CFBP (MCFBP) algorithm for highly squint spotlight SAR imaging. We employ a transformed Cartesian coordinate system to treat the transceiver mode as approximately side-looking mode. According to the transformed coordinate system, we propose a modified RM (MRM) to reduce the range error. Based on the MRM, we derive modified image spectrum compression steps and the Nyquist sampling rate of the subaperture image. MCFBP inherits CFBP's accuracy and efficiency advantages. Results of simulations and experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the improved performance of the proposed algorithm relative to BP and fast factorized BP.

Interrupted FMCW (IFMCW) SAR mode, which only employs a single antenna from a single micro-satellite, was proposed by Ahmed et al., recently. However, periodical data gaps will occur in the interrupted mode, caused by the process of switching the radar transmitter on and off. To solve this problem, in this paper, a modified aperture-interpolation -based approach is proposed to fill the data gaps. The approach can recover the gapped data with excellent performance and thus significantly suppress the artifacts (or ghosts) induced by the periodical data gaps. Processing results of real data acquired with an experimental airborne FMCW SAR system, demonstrate the effectiveness of the proposed approach.

A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.

The next generation of spaceborne synthetic aperture radar (SAR) remote sensing systems will emphasize on high-resolution and wide-coverage imaging. For these design goals, variable pulse repetition interval (PRI), which is also called staggered SAR, is a promising candidate to deal with the inherent problem of blind range effect in conventional SAR with constant PRI. This letter presents an improved work of signal processing to cope with two unavoidable problems in staggered mode: the loss of echo signal and nonuniform sampling in azimuth. Since the existing signal processing methods are mostly based on a high oversampling ratio, which would increase the range ambiguity to signal ratio and the amount of data to be downlinked, this letter focuses on signal processing with low oversampling factors. A two-step processing scheme - recovery and reconstruction - is proposed to recover the missing sampled data using the spectral-estimation techniques first and then reconstruct the in-band energy and minimize the ambiguity energy, which is from band unlimited SAR raw data, to obtain uniform sampling in azimuth. Point target simulations and artificially gapped data generated from China's S-band spaceborne SAR system HJ-1-C are shown to validate the proposed scheme.

The multitemporal interferometric synthetic aperture radar (InSAR) technique is a potential tool for measuring digital elevation models and surface deformation. It has the advantage of high precision, competitive spatial resolution, and wide coverage. To improve the accuracy of the final results, some adaptive multilook strategies have been proposed in which the identification of statistically homogeneous pixels (SHPs) is the key task. However, these methods are not always reliable in the case of small data sets. To improve this reliability, SHPs are identified based on the adaptive joint data vector comprising of temporal sample and spatial information in this letter. Additionally, the formulation of adaptive joint data vector is combined with local spatial features of SAR images. The presented adaptive multilook approach can be used in many interferometric applications, such as InSAR data filtering and coherence estimation. Experiments on six TerraSAR-X stripmap images of Tianjin in China validate the feasibility and effectiveness of the proposed approach.

According to the minimum antenna area constraint, synthetic aperture radar (SAR) systems require a low-pulse repetition frequency (PRF) to image the wide swaths in ocean surface monitoring scenarios. However, the low PRF that is lower than the Doppler bandwidth will cause azimuth ambiguities. This letter proposes a novel method of mitigating azimuth ambiguities when using an undersampled SAR system for ship detection over the open sea. In this letter, the concept of range subspectra is adopted to misregister the azimuth ambiguity signals. In addition, a change detection method that uses a principal component analysis and k-means clustering is adopted to detect differences in the azimuth ambiguities between two range subspectra images in which the azimuth ambiguities are misregistered. By compensating for the ambiguities in the corresponding image, the ambiguities can be mitigated. This method is only appropriate for bright targets over dark backgrounds in which residual energy loss occurs for useful signals. Both simulated and real data processing results have validated the effectiveness of the proposed method and show that it is feasible for maritime surveillance.

This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.

Due to the light weight and small size, synthetic aperture radar (SAR) sensors with light-small aircraft are very sensitive to atmospheric turbulences, which cause serious trajectory deviations. Limited by the cost or payloads, light-small aircraft sometimes cannot carry high-accuracy inertial navigation systems (INS)/global positioning systems (GPS). For the very high resolution (VHR) case, residual range cell migration (RCM) often exceeds several range resolution cells during the course of the synthetic aperture, thus degrading the image quality substantially. In this paper, a robust scheme for the correction of residual RCM is presented. The scheme consists of three principle steps. First, the range-compressed data in the signal domain are divided into subapertures in azimuth. For each subaperture, a sliding window is used to select the subblock data with the highest signal-to-clutter ratio. Second, for each subaperture, residual RCM is estimated by using the selected subblock data based on a nonparametric entropy minimization technique. Third, the estimated residual RCM of all subapertures are filtered and coherently combined to perform the correction for the full-aperture data. Processing the results of VHR SAR raw data shows that the proposed scheme is effective for highly precise imaging with light-small aircraft equipped with only low-accuracy INS/GPS.

Synthetic aperture radar (SAR) images require a high-resolution system for accurate interpretations. This high range resolution can be achieved by stepped frequency chirp signals. To reconstruct a wideband waveform from each subband signal, amplitude/phase/delay imbalance between the channels should be precisely compensated. In this paper, the system configuration is first presented. Further, a calibration strategy was proposed based on three calibration loops: the reference calibration, transmitting calibration, and receiving calibration loops for coarsely compensating the channel imbalance. Then, two different methods based on the cost functions were proposed to remove the residual channel imbalance. The proposed methods were validated using stepped frequency SAR data acquired by an X-band airborne SAR system with a total bandwidth of 3.6 GHz, yielding (unweighted) a 3-dB range resolution of 4 cm.

According to the characteristics of the Doppler history in ScanSAR mode, a novel ship detector is presented based on the raw echo data. The proposed approach is very efficient, since it does not need the computational imaging process. Its effectiveness is validated by an airborne ScanSAR real data set, collected by a C-band experimental system.

This manuscript focuses on velocity estimation for synthetic aperture radar (SAR) in high-resolution wide-swath mode. HRWS mode is proposed to overcome the system-inherent limitation, which allows for collecting additional samples under relatively low PRF. Unlike the previous work of SAR ground moving target indication (GMTI) in single channel and multiple channels, the velocity estimation in HRWS GMTI would face two inherent unavoidable problems: the under-sampled data in single channel and non-uniform samples in azimuth. The proposed method is effective, accurate and efficient under these two problems. Some relevant simulations are shown to validate the proposed scheme.

Coastline extraction with high accuracy from wide-swath synthetic aperture radar (SAR) imagery is still a challenging problem due to speckle, sea state condition, and land type. This letter presents a novel approach for coastline extraction from SAR images with complex scenarios and large geograp hical coverage, using the combination of modified K-means method and adaptive object-based region-merging mechanism (MKAORM). First, a modified K-means method is used to produce initial oversegmentation for the following region-merging stage. Second, an adaptive and coarse-fine object-based region-merging scheme using subregion classification is exploited to extend the automatically selected “sea” seed and “land” seed, respectively, to extract the final coastline. MKAORM can reduce the high computation cost of coastline extraction from wide-swath SAR imagery while keeping high accuracy. More than 93% of the detected coastlines lie within 2-pixel distance in comparison with the manually traced coastlines in experiments taken on Sentinel-1A (S1A) IW (Interferometric Wide-swath) mode SAR imagery.

A backprojection algorithm (BPA), a time domain synthetic aperture radar (SAR) imaging algorithm, can be widely used without modification in various imaging modes, like stripmap, spotlight, sliding spotlight, etc. However, BPA also suffers when used in azimuth multichannel SAR due to the nonuniform sampling problem caused by a nonideal pulse repetition frequency. In this letter, we propose a weighted BPA (WBPA) for azimuth multichannel SAR imaging. Derived from the filter-bank-based reconstruction method, WBPA adds a weighting procedure to BPA. Simulations and an airborne SAR data experiment demonstrate that WBPA can perfectly suppress the azimuth ambiguities under band-limited circumstances. WBPA extends the applicable scope of BPA.

Statistically homogeneous pixels (SHPs) are considerably significant in many interferometric applications, such as interferometric filtering, distributed scatterer selection, small baseline subset, and SqueeSAR processing. It is very important to achieve SHPs efficiently and accurately. Previous studies on SHPs' selection are based on spatial restrictions and likelihood ratio test, such as Lee filtering, Kolmogorov-Smirnov test, and Anderson-Darling test. However, these algorithms do not stand up to test for the spatial similarity hypothesis for complex terrains with a few images. To solve the problems, this letter proposes a modified SHPs' selection algorithm. It utilizes geometric distance and target features for reaching a priori information to help the similarity hypothesis tests. The proposed algorithm has been tested on simulated and real data to prove the improvements in terms of accuracy and computational efficiency.

For synthetic aperture radar (SAR), a system impulse response with low sidelobes is very important because sidelobes may interfere with the nearby scatterers and contribute to multiplicative noise. It is well known that a nonlinear frequency-modulation (NLFM) chirp waveform can shape the signal's power spectral density and provide a radar matched filter output with lower sidelobes without loss of the signal-to-noise ratio when compared with the linear frequency-modulation chirp. These advantages make the NLFM waveform to be a promising candidate to improve the imaging quality for SAR. However, so far, to our knowledge, there is no real application of NLFM waveforms for SAR. This letter, for the first time, demonstrates the airborne SAR experiment using an NLFM waveform. In the underlying experiment, the construction of the NLFM signal is investigated and a modified range migration algorithm (RMA) is developed to adapt it for focusing the NLFM SAR data. Both simulation and experimental results exhibit the promising power of the NLFM chirp and show the accuracy of the proposed modified RMA.

This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching ± 2.2°, several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

This paper proposes a synchronization algorithm for bistatic synthetic aperture radar (BiSAR) imaging in a spaceborne/stationary configuration. In real bistatic systems, synchronization errors are generally introduced into the received data. Additionally, the lack of precise imaging parameters, such as the position of the transmitter and the accurate sampling time, could affect the imaging quality greatly. Fortunately, the image could be well focused by the proposed algorithm in the case of lack of the accurate position of a transmitter and the sampling time. First, a preprocessing step is employed to remove synchronization errors through matching an echo signal with a direct signal. Then, a modified chirp scaling factor containing an error phase term is constructed, and the accurate position of the transmitter and the sampling time can be acquired by the phase extraction of the direct signal and the searching method based on contrast optimization. After that, the corresponding imaging process can be implemented. Finally, the proposed algorithm is validated by the simulation and experimental results, where TerraSAR-X is used as the illuminator.

This letter presents a new filtering technique for interferometric synthetic aperture radar (InSAR) phase images. Traditional local denoising algorithms all suffer from the drawback of removing texture detail information. In contrast, several nonlocal methods have attained good performance in InSAR applications. However, these methods only take radiometric similarity. The patch-based locally optimal Wiener filter (PLOW) utilizes both geometrically and radiometrically similar patch information by clustering analysis and nonlocal filtering; thus, it can better balance between detail preservation and denoising. Nevertheless, PLOW itself is not fitted for InSAR. In this letter, we modify and improve the original algorithm while considering the coherence coefficient and the characteristics of InSAR. This new method provides better filtering results, but with a higher computational cost. Moreover, we introduce the box dimension in fractal geometry as a new index. Experiments on simulated and real data demonstrate that this algorithm outperforms traditional denoising methods.

In this letter, an improved full-aperture imaging algorithm for scanning synthetic aperture radar (ScanSAR) mode is proposed, which fills the data gaps between bursts through a linear-prediction-model-based aperture interpolation technique in a subaperture manner before azimuth compression. It can significantly suppress the spikes induced by periodical data gaps and, at the same time, enhance the signal-to-noise ratio of the obtained ScanSAR imagery. This approach has a great potential in the interferometric context. The effectiveness of the proposed approach is demonstrated by both simulated and real ScanSAR data with different types of terrain. All the experimental data were acquired by the C-band SAR system with a bandwidth of 200 MHz, which was developed by the Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences.

In order to enable a testbed for spaceborne scanning synthetic aperture radar (ScanSAR) mode, in this letter, a ScanSAR autofocusing approach for airborne platforms has been developed. Autofocusing algorithms, such as the phase gradient autofocus (PGA) algorithm, prove to be a useful postprocessing technique to get refocused synthetic aperture radar images. However, conventional stripmap PGA does not work in ScanSAR mode when the full-aperture approach is used, due to the periodic data gaps in each subswath. To solve this problem, we extend the stripmap PGA to ScanSAR with some modifications, mainly in the subaperture segmentation and phase error estimation steps. The performance of extended stripmap PGA is evaluated by an airborne ScanSAR data set containing different types of terrain, with a high spatial resolution up to 3.5 m in azimuth.

A fully polarimetric response of landslide areas at X-band was studied by a Chinese high-resolution airborne synthetic aperture radar system. Polarimetric decompositions, including the Yamaguchi four-component decomposition and the Cloude decomposition, are used to analyze the scattering mechanisms of several typical landslides caused by the 2008 Wenchuan Earthquake in southwestern China. The experimental results indicate that areas affected by large-scale landslides show complicated scattering mechanisms at X-band, which are a mixture of surface, double bounce, and volume scattering. Simple classification results based on supervised Wishart classifier and polarimetric scattering similarity parameters are also presented, which can distinguish landslide areas from others, such as forest and water, very well. However, it does not perform well for urban areas. Additional information, such as prelandslide imagery, is needed to distinguish landslide areas from urban areas or bare soil. From these results, we can conclude that landslide mapping using fully polarimetric data has great potential for rapid response and management of landslide disasters.

Interferometric inverse synthetic aperture radar (InISAR) can achieve 3-D imageries of maneuvering target. However, the quality of 3-D imageries suffers from the noise and sidelobes seriously. In addition, the defocusing effect due to the nonlinear moving of the target can also bring into a bad effect for 3-D imaging. To tackle these issues, a 3-D InISAR imaging approach via compressed sensing (CS)-based super-resolution (SR) ISAR imageries is proposed. First, we establish the target sparsity-constraint optimization model containing both amplitude and phase information of scatterers. Second, a Bayesian CS approach is adopted to get high-quality SR ISAR image. The quasi-Newton solver guarantees both high amplitude and phase information recovery precision. At last, a high-quality 3-D view of the target is achieved via the conventional ISAR imagery pair interferometry technique. Computer simulation and real data experimental results verify the effectiveness of the proposed method.