Aircraft target detection in synthetic aperture radar (SAR) images is challenging due to discrete scattering points and complex environments. With neural network advancements, this task has become more feasible, although the existing networks often have too many parameters for performance-constrained devices like drones or sacrifice performance to reduce parameter count. To address this, we propose a lightweight multistage SAR aircraft target detection network, termed MSADNet, featuring a novel multistage asymmetric aggregation network (MAAN) module. The MAAN module enhances feature extraction through an improved asymmetrical bottleneck network and introduces a multiorder attention mechanism to focus on critical features. By carefully managing the shortest and longest gradient paths, the MAAN module achieves an organic integration of both, significantly improving network performance. The backbone network is constructed using the MAAN module, and the neck network employs a feature pyramid network (FPN) + PAN structure, integrating the MAAN module with a coordinate attention (CA) module. An anchor-free detection head is used for aircraft target detection. Testing on the SAR-AIRcraft-1.0 dataset demonstrates that MSADNet achieves a mean average precision (mAP) of 96.15% with only 4.88 M parameters. Compared to YOLOv8-s, MSADNet improves mAP by 0.61 percentage points while reducing the parameter count by 6.29 M, indicating high-performance detection with a low parameter count.

The time-series InSAR technique is commonly used for surface subsidence monitoring, providing insights into the trends of environmental changes. In this study, we employed the time-series InSAR technique in the Hunshandake Sandy Land, which will help evaluate the effectiveness of desertification control measures. The results show that there is no significant widespread subsidence or uplift in the Hunshandake Sandy Land, indicating limited movement of sand particles and minimal impact from human activities. However, within certain small areas of the study region, notable surface subsidence and uplift phenomena were observed, which can be attributed to saline-alkali land presence. Additionally, InSAR monitoring revealed pronounced decorrelation in the crescent-shaped dune region, suggesting rapid surface changes occurring on these dunes.

Synthetic aperture radar (SAR) has a wide range of applications in resource exploration, environmental monitoring, urban and rural planning, among others. However, SAR images often suffer from speckle noise, which requires the use of despeckling techniques. With the increasing availability of SAR time series, there is potential to develop more efficient despeckling methods. Nevertheless, in speckle reduction, the coherence between multitemporal SAR images creates new challenges. In this study, a patch-based low-rank approximation (PLRA) method is proposed for SAR time series despeckling using the RABASAR framework, which effectively eliminates temporal fluctuations and speckles. First, a similar patch search approach in time series is introduced to remove time-dependent changes by analyzing fluctuation models. Then, a low-rank approximation method based on patch stacks is proposed to obtain a low-rank image for noise filtering. Furthermore, the low-rank image is integrated into the RABASAR framework to improve the despeckling process. Experimental results demonstrate the superior performance of the proposed method in preserving image texture details, mitigating temporally correlated disturbances, and reducing speckle noise in comparison to other state-of-the-art methods.

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches.

Object detection in remote sensing images (RSIs) is crucial for ground observation applications such as land surveying, urban planning, and precision agriculture. With the advent of convolutional neural networks, the performance of object detection in RSIs has been significantly improved. However, most studies have focused exclusively on the feature extraction, processing, and representation of geographical objects. The lack of integration of relevant common-sense knowledge has led to some obvious missed detections and illogical false alarms in the detection results. This letter proposes a novel and widely applicable approach for updating and fusing object co-occurrence knowledge into a two-stage multi-class object detection framework, called Auto Learner of Co-occurrence. The method captures the correlation between different object categories during the training process with the Co-occurrence Knowledge Updating module and utilizes the established co-occurrence weight matrix to enhance classification with the Knowledge Fusing module. The effectiveness and adaptability of the proposed method were validated through experiments on two multi-class object detection datasets using four popular two-stage detectors. The experiments also indicate that the speed penalty and the parameter increase resulting from the method are negligible.

Azimuth multichannel synthetic aperture radar (SAR) working in beam steering modes can effectively obtain images with wide unambiguous swath and high azimuth resolution. However, azimuth phase center fluctuation (APCF) phenomenon can be induced by the beam rotation in azimuth. Consequently, addition phases will be introduced in echoes of different channels. Different beam steering laws result in different degrees of APCF phenomenon, which lead to different azimuth channel phase errors. These phase errors lead to paired azimuth ambiguities or targets offset in imaging results. In this letter, the effects of different beam steering laws on the imaging results are analyzed, and the total additional wave paths required by different beam steering laws are compared, aiming at providing a reference for the design of the practical beam steering SAR system. The impacts of different beam steering laws on the imaging results are verified by simulation experiments.

Azimuth multichannel synthetic aperture radar (SAR) working in the sliding spotlight mode can effectively achieve ultrahigh resolution and wide swath imaging. However, multichannel sliding spotlight SAR echoes with large pulse bandwidth and azimuth steering angle are difficult to reconstruct and process due to the large range frequency-dependent Doppler centroid varying frequency support. To resolve this problem, two azimuth preprocessing approaches both based on the range frequency-dependent deramping strategy are proposed in this article. In both approaches, echoes are handled by a range frequency-dependent azimuth deramping function at first, which assign the signal measurements to unambiguous Doppler frequencies. Afterward, in the first preprocessing approach, azimuth multichannel raw data can be reconstructed and combined before azimuth upsampling in the conventional two-step preprocessing technique. The other way to handle the deramped multichannel raw data is the modified full aperture azimuth multichannel sliding spotlight reconstruction approach, which embeds azimuth multichannel reconstruction into azimuth convolution between the deramped raw data and the selected range frequency-dependent azimuth deramping function. Furthermore, the azimuth data in the second approach should be resampled to obtain a uniform azimuth sampling interval. Computational complexity and effects on the following imaging algorithm of both approaches are analyzed and compared. Simulation results validate the proposed two azimuth multichannel preprocessing approaches.

Staggered synthetic aperture radar (SAR) can effectively stagger range blind areas by periodically changing the pulse repetition interval (PRI), to achieve continuous imaging with an ultrawide swath. In conventional staggered SAR, azimuth samples are nonuniformly distributed due to the variable PRI, and complex resampling processing, such as best linear unbiased (BLU) interpolation and multichannel reconstruction processing, is required. In this article, a strict rule of PRI variation and phase center adjustment (PCA) is proposed for staggered SAR, to solve the problem of nonuniform azimuth sampling. In the proposed joint strategy of PRI variation and PCA, the relevant parameters for PCA are first determined. Then, the PRI sequence is designed according to PCA parameters to stagger blind ranges. Finally, the PCA rule is designed according to the designed PRI sequence and PCA parameters. During the data acquisition interval, the effective phase center is periodically adjusted pulse by pulse according to this rule, making the azimuth samples totally uniformly distributed. In addition, the discontinuously distributed missing samples can be estimated by signal estimation approaches. With the proposed joint rule of PRI variation and PCA, the emergence of false targets in imaging results can be avoided, and the complex resampling processing can be relieved to reduce the computational complexity. Simulation experiments on imaging results verify the advantages of this strategy.

Radio frequency interference (RFI) sources pose threats to wideband synthetic aperture radar (SAR) systems and accurate SAR image interpretation. Since most of RFI sources are narrowband, notch filtering is a simple but effective method for RFI suppression. In this letter, a modified two-step notch filtering approach combined with linear prediction is proposed to improve the SAR image quality. The notch filtering is used to mitigate narrowband RFI energy, while the linear prediction is introduced to recover the missing range spectral component of the SAR raw data from the desired scene, which is removed together with RFI sources by the notch filter. Because of the Gibbs phenomenon in Fourier series, the small residual RFI energy after notch filtering is enough to cause image visual disturbances and affect the accuracy of the following missing range spectrum extrapolation. The two-step notch filtering with a limited bandwidth is applied for better RFI sources mitigation. Simulation results on both simulated targets and real SAR raw data validate the proposed approach.

Array SAR can overcome the problems such as overlay and shadow caused by the terrain that the traditional SAR observes with large changes in slope. However, compared with traditional SAR, array SAR inevitably has multi-channel amplitude and phase errors due to its large number of channels. If these errors are not corrected, radar imaging will be degraded. This paper proposes a method based on time-domain processing to compensate for possible errors in the time domain. Finally, the effectiveness of this method is verified by CS algorithm experimental simulation.

Compared with the conventional 3-D synthetic aperture radar (SAR) system, the arc 3-D SAR can achieve a wide azimuth observation extent. In this letter, a far-field pseudopolar format imaging algorithm for the ground-based arc 3-D SAR is presented. Different from the existing algorithms, the proposed algorithm exploits keystone formatting and fast Fourier transforms (FFTs) to obtain a pseudopolar grid in the elevation direction, then complex multiplications and FFTs are performed in the range-azimuth wavenumber domain to complete 3-D focusing. The advantages of this method are its low computational complexity and high efficiency. The sampling criteria and computational complexity are also discussed in this letter. Finally, the performance of the proposed algorithm is validated with numerical simulations.

According to the characteristic of different air bubbles and sediments content in the Yellow River ice (YRI), the YRI is classified into three types: thermal ice (TI), frazil ice (FI) and consolidated ice (CI). In this paper, we are modeling YRI firstly, and the backscattering coefficient of the ice cover based on this model is deduced on the basis of the integral equation model (IEM) and the vector radiation transfer theory (VRT). Due to the characteristics of different types of YRI corresponding to different microwave electromagnetic scattering, a method of classification of YRI based on decision tree is proposed. The method uses horizontal polarization backscattering coefficient, correlation coefficient and cross polarization power to classify different types of YRI. Finally, the classification accuracy is evaluated by using the confusion matrix (CM). The results show that the classifier is effective for the YRI classification.

It is difficult to classify Hunshandake sandy land due to complex terrain, an improved classification method is proposed in this paper to promote the effect of classification. Firstly, scattering characteristics of ground objects are extracted. Then, the extracted features is combined to Jensen-Bregman logDet(JBLD) for classification. Finally, Hunshandake sandy land obtained by Radarsat-2 satellite is classified to verify the effectiveness of the method.

Ground-based SAR has become an important remote sensing technology means for deformation monitoring in recent years. However, in order to achieve higher efficiency data acquisition and wider observation, arc antenna array technology is applied to ground-based SAR. In this paper, a polar format imaging algorithm for arc antenna array ground-based SAR is proposed. Firstly, a brief background on ground-based SAR and arc antenna array are provided. Then, the signal model and imaging algorithm of arc antenna array ground based-SAR are introduced. Finally, simulation result is presented.

At present, various models are developed for soil moisture retrieval, but the application of polarimetric SAR data to retrieve soil moisture in sandy land is rare. Therefore, it is necessary to develop a method for retrieving soil moisture in sandy land. In this paper, we proposed a model to estimate the soil moisture in Hunshandake Sandy Land. The model does not need to take into account the surface roughness, only using the VV and HH polarization backscattering coefficients can be used to retrieve soil moisture. In addition, diversity of the vegetation over the sandy land is difficult to predict. We selected five samples to analyze the effect of vegetation on the inversion results of soil moisture in sandy land.

Downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) can obtain 3-D scene properties and has broad application prospects. However, the reconstruction of cross-track dimension usually suffers from incomplete observation, which is caused by the non-uniformly and sparsely distributed virtual antenna phase centers. By formulating the cross-track reconstruction into the problem of sparse signal recovery, we introduce two kinds of gridless sparse recovery (GL-SR) methods to DLSLA 3-D SAR cross-track imaging, i.e., atomic norm minimization (ANM) and gridless SPICE (GLS). Compared with the conventional grid-based sparse recovery (GB-SR) methods, which assume that the scatterers are exactly on the discretized grids, the GL-SR methods can avoid the off-grid effect. Experiments compare the performance of GB-SR and GL-SR methods for DLSLA 3-D SAR cross-track reconstruction.

Natural geological disasters such as landslide, mudslide and man-induced mine collapse, landslide and other mountain deformations seriously endanger the personal safety and property of people. GB-SAR (Ground-Based Synthetic Aperture Radar) imaging radar has been developing as an important technical for deformation monitoring. In this paper, a novel multi-aspect ground-based SAR deformation monitoring system is developed and introduced. Firstly, a brief background on ground-based microwave imaging radar for high accurately deformation monitoring is provided. Then, system configuration is given. Finally, some preliminary experiments are presented, and the obtained data is processed to further demonstrate the potential capability and precision of the system.

The target feature is sensitive to the aspect angle of SAR observation, making the interpretation and target recognition of the SAR image difficult. The information acquired from a certain aspect angle is partial and incomplete, and the multi-aspect observations have the potential to improve the SAR performance in this aspect. Three topics of fine feature description of multi-aspect SAR observations are discussed, and they are the 3D information extraction, the optimum imaging strategy for anisotropic scatterers, and the multi-aspect scattering feature extraction. The initial results of the real P band airborne circular SAR (CSAR) data and the turn table data show that multi-aspect SAR observations have the encouraging potential capability in target fine feature description.

Multi-aspect observation can obtain the target's multi-aspect scattering feature, and has potential to improve the SAR performance in the applications of target classification and recognition. This paper presents a time-division scanning mode based on Single Phase Centre Multiple Azimuth Beam (SPC-MAB) technique. By applying this new mode, Synthetic aperture radar (SAR) can obtain multi-aspect data from continuous scenarios through linear flight. We take six channels as an example, and analyzing the performance of the time-division scanning mode and two conventional SPC-MAB modes. According to noise equivalent sigma zero (NESZ) and azimuth ambiguities to signal ratio (AASR) simulation results, the time-division scanning mode shows better performance than conventional modes. The time-division scanning mode will be applied to the multi-aspect data acquisition vehicle-mounted system.

Airborne downward-looking linear-array three dimensional synthetic aperture radar (LA-3D-SAR) can achieve high-resolution three dimensional imagery with a uniform antenna array. However, the actual 3D imagery is unavoidably degraded by amplitude-phase errors due to non-ideal antenna characteristics and motion measurement deviations. This paper investigates the effects of these errors on the forms and the degrees of image quality degradation, and considers the use of corresponding calibration methods to eliminate the effects of errors. Two calibration methods are proposed, which are based on external parallel and point target calibrators, respectively. At last, real data experiments have shown the validity of the analyses as well as the effectiveness of the proposed calibration methods.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) operates downward-looking observation and obtains the 3-D microwave scattering information of the observed scene. The cross-track physical sparse linear array is often configured to obtain uniform virtual phase centers in order to adopt the frequency-domain algorithm. However, the virtual phase centers usually have to be nonuniformly and sparsely distributed due to the array elements' installation locations restricted by the airborne platform and the airborne wing tremor effect. In this state, the frequency-domain algorithm cannot be directly used. In this paper, a DLSLA 3-D SAR image reconstruction algorithm that combines polar formatting and L1regularization is presented. Wave propagation and along-track dimensional imaging are first finished after polar formatting and wavefront curvature phase error compensation; then, cross-track dimensional imaging is completed with the L1regularization technique. The proposed algorithm is applicable to airborne DLSLA 3-D SAR imaging under nonuniformly and sparsely distributed virtual phase centers condition. The proposed algorithm was verified by 3-D distributed scene simulation experiment (P-band circular SAR image was selected as radar cross-section input, and X-band digital elevation model of the same area was selected as the coordinate positions of the scene) and the field experiment. Image reconstruction results and image reconstruction performances, such as normalized radar cross section, height errors, and orthographic projection image grayscale distribution, are demonstrated and analyzed with different signal-to-noise ratios, different array sparsity, and the incomplete compensated residual oscillation error 3-D distributed scene simulation experiments. Simulation and field experimental results show the good performance in focusing and the robustness of the proposed algorithm.

The helicopter has wide application foreground in disaster relief owing to its good maneuverability. This paper firstly presents basic principle of MIMO-SAR based on arc antenna array and system configuration in details, establishes the corresponding imaging model and provides a particular antenna configuration of arc antenna array installed on helicopter. Then the responding system resolution theory, sampling criteria and performance parameter of the imaging mode are analyzed and a novel imaging algorithm is developed combined with arc aperture configuration. Finally, during platform takeoff and landing, high resolution image acquisition abilities are demonstrated by numerical simulations. On this basis, a ground based experimental system is constructed, also the feasibility and validity of the proposed mode is demonstrated by mean of experimental data acquisition and processing.

In order to achieve 3-D imaging with an airborne down-looking linear-array synthetic aperture radar (LASAR), a uniform virtual antenna array may be obtained by aperture synthesis of the cross-track sparse multiple-input-multiple-output array. However, the actual 3-D imaging quality is unavoidably degraded by errors in the virtual element position. In this letter, we investigate the effects of these errors on the forms and the degrees of image quality degradation by decomposing the error-related stochastic processes via an orthogonal transform based on discrete Legendre polynomials. It should be noted that these analyses are helpful for designing a LASAR system and providing a reference for specifying the requisite precision of measurement devices and calibration methods. Finally, we briefly consider the use of calibration methods to eliminate the effects of errors.

In this letter, a novel 3-D imaging algorithm for down-looking linear-array synthetic aperture radar is presented. The usual algorithms involve either time-consuming interpolations or huge oversampling operations, which adds huge burden to the image formation processor. The proposed algorithm, in contrast, is not only accurate because little approximation has been made but also quite efficient because of two major endeavors that we have made: First, a novel 3-D pseudopolar domain is introduced for the focused data to avoid any oversampling and to make it possible to formulate the image formation process as an interpolation-free image series expansion, and second, convergence acceleration theory in computational mathematics is introduced to accelerate the convergence of the image series expansion. The 3-D imaging capability is evaluated, and the validation of the proposed algorithm is done by exploiting simulated data.

Airborne downward-looking sparse linear array 3-D synthetic aperture radar operates nadir observation and obtains the 3-D microwave scatter information of the observed scene. A polar format algorithm (PFA) with space-variant wave-front curvature phase error compensation is presented. A 3-D image in polar coordinate can be obtained with the proposed PFA, and a 3-D image in Cartesian coordinate can be obtained with interpolation. The proposed PFA possesses the advantages of high precision, low memory requirement, and low computational complexity. The focus performance of the proposed PFA is validated by 3-D distributed scene simulation with an airborne X-band digital elevation model and a P-band circular SAR image of the same area as simulation scene input.

This letter proposes a new method for multiband radar signal fusion by making use of all-phase fast Fourier transform (apFFT) algorithm and iterative adaptive approach (IAA). Central to the proposed method are, first, the mutual incoherence compensation between various subbands by apFFT algorithm, and second, the application of IAA to the mutually coherent subband measurements for signal fusion. Taking advantage of both algorithms, the proposed method effectively improves the range resolution with low sidelobes and performs robustly in the presence of noise. In particular, it requires no model information and therefore enables flexible implementation for practical application. The feasibility and effectiveness of the proposed algorithm are validated through both numerical simulations and raw data processing results.

The first airborne Circular SAR data acquisition experiment of China was carried out in Sichuan by the National Key Laboratory of Science and Technology on Microwave Imaging (MITL), China. Data was acquired using the MITL's P-band, fully polarimetric SAR system along a circular trajectory with the beam spotted on the same area. Compared with the conventional SAR along a straight path, this imaging mode mainly has the following attractive features. First, observing from all directions can help for a better understanding of the scattering properties of targets. Second, the wide angular aperture obtained via flight in a circular track makes possible high resolutions with low frequency band. Third, the aspect angle diversity inherent to the circular track allows for a 3-D target reconstruction. This paper presents the SAR processing of such data, and shows several results to analyze the potentials and limitations of such imaging geometry.

This letter presents a new algorithm for squint circular synthetic aperture radar (SAR) (CSAR) imaging, which is an extension of the well-known range migration algorithm. Due to the circular trajectory, the spatial frequency domain data of squint CSAR cannot be readily obtained via fast Fourier transform, as conventional SAR with straight path does. This method first employs along-track varying system kernels and filters to transform the raw data to the polar spatial frequency domain. Then, it uses an interpolation algorithm to convert the polar samples into rectilinear samples. Implementation aspects, including sampling criteria, resolutions, and computational complexity, are also assessed in this letter. The proposed algorithm is validated both numerically and experimentally.

The aperture of 360° gives circular synthetic aperture radar (SAR) (CSAR) the capability to detect hidden target when its orientation is unknown. Subwavelength resolution can also be achieved when the target in the spotted area is observed under a complete circular aperture. Furthermore, the aspect angle diversity inherent to the circular trajectory makes possible a 3-D target reconstruction. However, the latter two potentials require certain target reflectivity homogeneity. For a highly directive scatterer, it has no resolving ability in the direction normal to the data collection plane. In this letter, a new interferometric CSAR method is presented to enhance the tomographic imaging capability for highly directive scatterers without sacrificing other scatterers' resolutions. This method takes advantage of the coherence and the phase difference between a pair of 3-D SAR images formed from data collected at two separate circular apertures to eliminate targets that focused at a wrong elevation. In addition, it uses two different transmit frequencies to solve the problem of phase cycle ambiguities. Finally, simulation results validate this new approach.

In this paper, a three-dimensional (3-D) F-SAR imaging algorithm is introduced for Forwarding-looking SAR (F-SAR) data processing. The algorithm is the extension of the two-dimensional (2-D) range stacking algorithm (RSA) and allows the accurate image reconstruction without any interpolation and geometric correction. Then the spatial-varying and spatial-shift properties of the 3-D spread point function (PSF) of Forwarding-looking SAR are revealed with the analytical expression. Finally, the simulation experiment is performed to demonstrate the validity of the 3-D RSA.

In this paper, we consider the super resolution millimeter wave spotlight SAR imaging problem through the simulation under the given Strap-down Inertial Navigation System/Global Positioning System (SINS/GPS) and introduce the Phase Gradient Algorithm (PGA) into the Back-Projection (BP) for SAR focusing. The imaging results indicate that it is feasible to obtain super high resolution imagery at the millimeter wave using the SINS/GPS.