Tomographic synthetic aperture radar (TomoSAR) enables high-resolution 3-D reconstruction of mountainous terrain, but TomoSAR point clouds often suffer from outliers and height errors due to estimation noise and geometric distortions. In this letter, we propose a framework combiningL1/2-norm outlier detection and moving least squares (MLS) interpolation. TheL1/2-norm effectively identifies sparse anomalies, while MLS reconstructs continuous terrain with preserved features. Compared with L1-norm regularization based outlier detection technology, theL1/2-norm method can obtain sparser solution, achieving an order-of-magnitude improvement in height reconstruction accuracy, and hence shows superior performance in 3-D imaging of mountain areas. Experimental results based on simulated and real Fucheng-1 SAR dataset verify the proposed method.

With the expansion of the surveillance area and resolution of spaceborne synthetic aperture radar (SAR) systems, the increasing amount of echo data requires further research on efficient imaging methods. The pulse repetition frequency (PRF) of traditional SAR needs to satisfy the Shannon–Nyquist sampling theory, while the PRF limits the system swath width, making it impossible to achieve wider illumination coverage. The reduction of the PRF can effectively increase the swath width, but it will cause severe azimuth ambiguity, decreasing the image quality. Several methods have been introduced for ambiguity suppression, but many of them become ineffective at lower PRFs. In this article, we propose a novel sparse imaging method for the spaceborne PRF-reduced sliding spotlight SAR. The proposed method considers the azimuth ambiguity term in the joint sparse imaging model, separately constrains the main imaging and azimuth ambiguity areas, and achieves azimuth ambiguity suppression by using compressive sensing (CS) technology. With its help, we can reduce the original PRF by up to half to double the swath width and enable high-precision sparse reconstruction of large-scale scenes. Compared with$L_{2,1}$-norm regularization-based algorithm, the proposed method shows the superior ambiguity suppression ability with less computational cost from PRF-reduced echo data. Experimental results on simulated raw data validate the proposed method.

Airborne synthetic aperture radar (SAR) systems are commonly susceptible to trajectory deviations, resulting in distinct azimuth phase error in the collected echo. SAR autofocus technology can compensate phase error and produce a well-focused image based on echo data. However, due to the influence of unfavorable factors such as radar interruption and electromagnetic interference, the echo data may be down-sampled in azimuth, which reduces the phase error estimation accuracy of traditional autofocus methods. By introducing compressed sensing (CS) to SAR data processing, sparse SAR imaging shows outstanding performance in acquiring high-resolution images utilizing down-sampled echo. However, the phase error existing in azimuth direction will reduce the sparsity of observed scene, and the precision of sparse reconstruction is consequently decreased. This paper aims at enhancing the Doppler rate error estimation precision when echo is down-sampled in azimuth and proposes a novel sparse imaging method combined with Doppler rate estimation. During each iteration of complex approximated message passing (CAMP) algorithm, the Doppler rate error is estimated according to the non-sparse solution by fractional Fourier transform (FrFT). Then phase error is compensated to the non-sparse solution, and the azimuth matched filtering (MF) operator is upgraded. The aforementioned steps are performed iteratively until a well-focused sparse SAR image is generated. It should be noted that the Armijo rule and random sample consensus algorithm (RANSAC) are introduced to guarantee the fast and precise reconstruction of the observed scene. Experiments from simulated and airborne data prove the enhancement in Doppler rate estimation precision by the proposed method than traditional estimator when used data is azimuth down-sampled.

Synthetic aperture radar tomography (TomoSAR) is a common measurement technique for three-dimensional (3-D) imaging of forested areas. It extends the synthetic aperture principle into the elevation direction, obtains the elevation scattering information of the observed target, and then realizes the 3-D reconstruction. In order to achieve high-resolution imaging, compressed sensing (CS) technology is widely used in TomoSAR. Given that many existing forested area datasets comprise multi-polarization modes, leveraging the complete potential of polarimetric data to achieve high-resolution 3-D recovery of the observed scene is essential. Therefore, this paper proposes a novel L2,1/2 -norm regularization based TomoSAR imaging method of the forested area, which jointly reconstructs the multi-polarization data and utilizes the correlation between each polarization to obtain more accurate 3-D reconstruction results. Experimental results based on real BioSAR 2008 L-band dataset are used to verify the proposed method.

In three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the elevation reflectivity function of each azimuth-range pixel is typically recovered through a large number of data collected by parallel flight tracks. Especially for holographic SAR (HoloSAR) tomography with high-resolution and panoramic 3-D imaging capabilities, it provides many important features for applications such as urban monitoring and 3-D mapping. In recent years, the utilization of polarimetric SAR data has been found to have tremendous potential in the performance improvement of 3-D reconstruction. In this letter, we propose a novelL2,1-norm based polarimetric HoloSAR imaging method, which utilizes the structural correlation between the echoes acquired from different polarimetric channels to achieve high-quality 3-D scene recovery. Compared with typical spectrum estimation based and compressed sensing (CS) based methods that only consider the single-polarimetric data, the proposed method can improve the accuracy and robustness of 3-D reconstruction. Experimental results based on real GOTCHA dataset verify the feasibility and potential of the proposed method.

Data-driven algorithms based on deep neural networks (DNNs) for ship detection in synthetic aperture radar (SAR) images are restricted by limited training samples and complex background interference. Inspired by the sparsity and neighborhood relevance of ships in SAR images, a novel detection algorithm based on self-attention dictionary learning (SADL) is proposed in this letter, which only requires a few samples for training. A self-attention mechanism is injected to learn discriminative features between classes, which can extract inherent information from sequences adaptively. Particularly, a hybrid loss function is tailored for the representations of multiclasses targets using SADL, which consists of the reconstruction error, minimal intraclass error, maximum interclass error, and exclusiveness error. Further, a SADL-based ship detection method is proposed by building subdictionaries of the target and background, respectively. The gradient and intensity information in a fixed neighborhood are used to construct the feature dictionary to suppress complex background interference and provide effective prior knowledge. Experiments conducted on the large-scale SAR ship detection dataset (LS-SSDD-v1.0) demonstrate the effectiveness of the proposed method, which achieves an F1-score of 0.47 on 3000 test images with only three training images.

The phase curvature autofocus (PCA) is an autofocusing algorithm with high efficiency and excellent robustness. However, its accuracy will be severely affected by the signal-to-noise ratio (SNR) of range-compressed data, and it is not suitable for downsampled data. In this letter, we propose a novel sparse autofocusing imaging method for stripmap synthetic aperture radar (SAR) phase error correction and image reconstruction. In the proposed method, the initial SAR image is first recovered by the Bi iterative soft thresholding (BiIST) algorithm, which is a sparse imaging technology that can suppress noise and maintain image phase information. Then, PCA is used to process the reconstructed image with phase information to estimate the phase error. Finally, the approximated observation-based sparse imaging method is used to obtain the precisely focused SAR image from the compensated full-sampled or downsampled echo data. Experimental results based on simulated data with adding different kinds of phase error show that compared with the reconstructed results of chirp scaling algorithm (CSA), traditional PCA, and iterative soft thresholding (IST)-based sparse imaging method, the contrast of the proposed method-based image can be improved at least 30%.

Unlike monostatic synthetic aperture radar (SAR), the instantaneous slant range of one-stationary bistatic SAR (OS-BiSAR) is determined by the position of the receiver, transmitter, and target. Therefore, the echo data of OS-BiSAR have strong 2-D spatial variability, which increases the difficulty of high-precision OS-BiSAR imaging. At present, the frequency-domain algorithm and its improved methods are no longer applicable, and it is impossible to perform high-precision imaging of OS-BiSAR. To solve above problems, in this letter, we propose a novel backprojection (BP)-based OS-BiSAR sparse imaging method. In the proposed method, an echo simulation operator is first constructed based on BP algorithm to achieve azimuth-range decoupling of the collected echo. Then, the considered scene is recovered by solving an$L_{1}$-norm regularization problem. Experimental results shows that compared with typical matched filtering (MF)-based OS-BiSAR imaging algorithm, the proposed method can obtain the SAR image with higher quality especially when the used data are downsampled and improves the image target-to-background ratio (TBR) at least 5 dB.

Various reasons can induce phase error during synthetic aperture radar (SAR) data collection. If phase error is ignored during SAR imaging, the defocused image will be obtained. Although conventional autofocusing methods can eliminate phase error in the full-sampled case, they are not suitable for down-sampled data. Sparse SAR imaging is a technique that combines sparse signal processing with SAR imaging. It can deal with both full- and down-sampled data if the underlying scene admits a sparse representation in a particular domain. Autofocusing methods based on sparse SAR have been developed in the last decades. However, they are known to be developed for spotlight mode. Furthermore, there is a phase ambiguity problem in existing methods, which will affect the uniqueness of the solution if not handled properly. In this paper, we propose a sparse SAR autofocusing imaging method suitable for stripmap mode and give a solution to phase ambiguity problem. The method jointly estimate phase error and reconstruct sparse SAR image in an iterative way. Each iteration consists of sparse imaging based on the phase error corrected echo and an update of the phase error estimation. Experimental results based on simulated and real data verify the effectiveness of the proposed method in coping with the full- or down-sampled data corrupted by the phase error.

Synthetic aperture radar (SAR) is an active surveillance system which is able to work under all-day and all-weather conditions. Due to the rapid development of SAR, automatic target recognition (ATR) as one of SAR image applications has become an important research direction in recent years, and its key process is target classification. In this paper, a novel target classification model based on amplitude and phase information of sparse SAR image is proposed. Firstly, we use a novel iterative soft thresholding (BiIST) algorithm to acquire sparse SAR image with better quality than traditional matched filtering (MF)-based SAR image. Moreover, the amplitude and phase information are utilized to classify the targets based on the proposed amplitude-phase convolutional neural network (AP-CNN). Experimental results based on real MSTAR dataset show that AP-CNN performs better than amplitude-based CNN under standard operating conditions (SOC). In the case of limited samples, the performance gap between the proposed network and amplitude-based CNN is more obvious.

Compared with traditional synthetic aperture radar (SAR), sparse SAR system can obtain higher swath with reduced pulse repetition frequency (PRF). Terrain observation by progressive scan (TOPS) is a novel and promising mode of wide-swath SAR application. It can achieve the same swath coverage as ScanSAR, but greatly reduces the scalloping. To achieve the high-resolution and wide-swath SAR imaging, this paper introduces a novel mode design framework of sparse TOPS SAR system. Combining the advantages of sparse imaging and TOPS, the proposed mode can obtain wider swath by sparse sampling in azimuth direction without changing other SAR hardware parameters, and simultaneously obtains the high-resolution sparse SAR image. The effect on DTAR from steering angle is also suppressed in this mode.

The thermal protection system (TPS) ensures the flight safety of the high-speed spacecraft. However, different degrees of damage are unavoidable during the flight missions. In this paper, a nondestructive detection of on-orbit spacecraft TPS with ISAR imaging technology is proposed. The TPS with micro-damage structures such as cracks, debonding, warpage, holes etc. are modeled, the corresponding Ka- and W-band backscattered electric field data are simulated, and finally the ISAR two-dimensional images are generated with the back projection algorithm. The experimental results have well verified that ISAR imaging can effectively achieve high-precision images of the damaged thermal protection structures. Thus, through the real-time ISAR imaging of targets such as space stations from accompanying satellites, the possible damage types and damage locations can be monitored and displayed to achieve nondestructive detection of TPS and to evaluate the flight safety.

Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with$\ell _{1}$-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via$\ell _{2,1}$-norm is proposed. Compared with$\ell _{1}$-norm -based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.

Different from optical image limited in time and space, sparse synthetic aperture radar (SAR) can obtain high-resolution image in all day and all weather conditions. Thus, SAR image has been widely used in military fields. With rapid development of technology and the growth of data volume, it is obviously impractical to interpret SAR image manually. Therefore, in this paper, we propose a novel sparse SAR automatic target recognition (ATR) framework, which is composed by the regularization sparse recovery algorithm and YOLO networks, so as to identify the SAR target quickly and accurately. In the proposed framework, we first use the complex approximate message passing (CAMP) based sparse image recovery algorithm to construct the sparse SAR image dataset, then identify the SAR targets by YOLOv3 and YOLOv4 networks. Experimental results based on MSTAR dataset validate the proposed framework effectively.

Using the continuous-wave technology to replace the conventional pulse-mode, frequency-modulation continuous-wave (FMCW) synthetic aperture radar (SAR) has shown good potentials of reducing the weight of the system and the sensors' peak transmission power. In order to relax the requirements of data bandwidth and storage, and increase the swath, the SAR system will collect the downsampled data, which makes the traditional matched filtering (MF)-based method unable to recover the considered scene, leading to failed reconstruction. To solve this problem, this letter presents an FMCW SAR sparse imaging method based on the frequency-scaling algorithm (FSA). Experimental results on the real data show that compared with the MF-based FMCW SAR imaging algorithms, the proposed method can improve the recovered image performance effectively. For the sparse surveillance region, it can achieve accurate recovery even from the downsampled data. Because the computational complexity of the proposed method is in the same order as that of MF, the sparse imaging of large-scale scenes can also be realized in the FMCW SAR.

Nowadays, because the frequency modulation continuous‐wave (FMCW) synthetic aperture radar (SAR) shows good potential in minimal transmission power and weight reduction of radar systems, it has been widely used and has become a common technique in modern short‐range high‐resolution earth observation. Different from the conventional pulsed mode, the stop‐to‐go approximation is not valid in FMCW SAR. Thus, the typical SAR imaging methods need to be modified to adapt the continuous‐wave scheme in practical data processing. In this letter, a wavenumber domain algorithm (WDA) is derived and used for the FMCW SAR imaging under squint and high‐squint cases. With the help of the exact scene recovery ability of the proposed WDA, we can achieve the accurate recovery of the considered scene even when using the collected high‐squint data and hence obtain the well‐focused high‐resolution image of the considered scene. Experimental results with simulated and airborne data verify the effectiveness of the proposed method.

In this letter, a novel subpixel mapping (SPM) based on extended random walker (ERW) (SPMERW) is proposed. First, the resolution of the original coarse remote sensing image is upsampled by bicubic interpolation. Second, the class proportions of subpixel are produced by unmixing the upsampled image. Irregular objects are generated by adaptive segmentation of the first principal component of the upsampled image. Third, the class proportions of the object are derived by averaged fusion of the class proportions of subpixel belonging to each object in the segmentation image. Object spatial dependence including the spatial information among and within the objects is obtained by the ERW algorithm. Finally, a class allocation method based on units of the object is utilized to obtain the SPM result according to the object spatial dependence. Experimental results on two remote sensing data sets show that the proposed SPMERW outperforms the state-of-the-art SPM methods.

In recent years, the sparse signal processing technique has shown significant potential in synthetic aperture radar (SAR) imaging, such as image performance improvement and downsampled data-based image recovery. However, due to the huge computational complexity needed, the existing sparse SAR imaging methods, such as conventional observation matrix-based and azimuth-range decouple-based algorithms, are not able to achieve real-time processing, especially for the large-scale scenes, which seriously restricts its application in some fields, e.g., real-time monitoring and early warning. To solve this problem, this article presents a novel real-time sparse SAR imaging method, which can get a similar image performance to that obtained by the existing sparse imaging methods, to reduce the computational complexity to the same order as that required by matched filtering (MF)-based algorithms. This means that with the proposed method, real-time data processing for practical large-scale scene sparse reconstruction becomes possible. Experimental results based on simulated and real data along with a performance analysis are presented to validate the proposed real-time sparse imaging method.

Frequency-modulation continuous-wave (FMCW) synthetic-aperture radar (SAR) can minimize the peak transmission power of sensors and reduce the size and weight of the systems. Wavenumber domain algorithm (WDA) is an accurate focusing method for SAR imaging. By using the exact signal form to compensate the phase error, WDA can achieve exact scene recovery from high-squint and long aperture data as long as the platform velocity is stable. In this paper, we introduce WDA to FMCW SAR and discuss the WDA-based FMCW SAR sparse imaging method. There are two main contributions of this paper: 1) a motion compensation-based WDA imaging method is introduced to correct the motion error that is often associated in practical airborne FMCW SAR data and 2) a novel WDA-based FMCW SAR sparse imaging method is developed to further improve the performance of recovered image. Compared with the typical WDA algorithm, the sparse imaging method can suppress the noise and sidelobes and perform the sparse scene recovery from the downsampled data. Experimental results via simulated and real airborne data verify the presented WDA-based FMCW SAR imaging methods.

Using sparse signal processing to replace matched filtering (MF) in synthetic aperture radar (SAR) imaging has shown significant potential to improve image quality. Due to the huge computational cost needed, it is difficult to apply conventional observation-matrix-based sparse SAR imaging method for large-scene reconstruction. The azimuth-range decouple method is able to minimize the computational complexity and achieve image performance similar to that obtained by the observation-matrix-based algorithm. However, there still exist two difficult problems in sparse SAR imaging, i.e., real-time processing and lack of raw data. To solve these problems, this paper presents a novel complex-image-based sparse SAR imaging method. It is found that if the input MF-recovered SAR complex image is obtained via fully sampled raw data, the proposed method can achieve an identical high-resolution image to that obtained by the azimuth-range decouple algorithm. The computational complexity is also decreased to the same order as that of MF, which makes the real-time sparse SAR imaging become possible. In addition, it should be noted that even though without raw data, the proposed method can still obtain impressive sparse recovery performance by using only the available complex image. Performance analysis and experimental results on real data validate the proposed method.

Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, Lq(0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional Lq-regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical Lqregularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L1regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L1regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.

This paper proposes a novel azimuth-range decouple-based L1regularization imaging approach for the focusing in terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR). Since conventional L1regularization technique requires transferring the (2-D) echo data into a vector and reconstructing the scene via 2-D matrix operations leading to significantly more computational complexity, it is very difficult to apply in high-resolution and wide-swath SAR imaging, e.g., TOPS. The proposed method can achieve azimuth-range decouple by constructing an approximated observation operator to simulate the raw data, the inverse of matching filtering (MF) procedure, which makes large-scale sparse reconstruction, or called compressive sensing reconstruction of surveillance region with full- or downsampled raw data in TOPS SAR possible. Compared with MF algorithm, e.g., extended chirp scaling-baseband azimuth scaling, it shows huge potential in image performance improvement; while compared with conventional L1regularization technique, it significantly reduces the computational cost, and provides similar image features. Furthermore, this novel approach can also obtain a nonsparse estimation of considered scene retaining a similar background statistical distribution as the MF-based image, which can be used to the further application of SAR images with precondition being preserving image statistical properties, e.g., constant false alarm rate detection. Experimental results along with a performance analysis validate the proposed method.

Microwave imaging, or synthetic aperture radar (SAR) shows its remarkable importances in various fields of remote sensing. Modern SAR system usually comes with high imaging resolution and wide mapping swath. This brings difficulties to the future development of SAR system. As a solution, the concept of SAR imaging under sparse constraint, or sparse microwave imaging radar is suggested, which is mainly the idea of introducing the sparse signal processing theory to the radar imaging. Under the sparse constraint, this technique could bring us benefits including better imaging performance e.g. lower ambiguity, higher resolution, lower side lobe and lower system complexity [1, 2, 3, 4].

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

Tomographic synthetic aperture radar imaging (TomoSAR) is a new SAR imaging modality that extends the synthetic aperture principle into the elevation direction. In TomoSAR, the elevation resolution depends on the length of elevation aperture and the baseline distribution. When the length of elevation aperture is fixed, the number of baselines is important for the elevation reconstruction. Therefore, improving the elevation imaging quality with the finite amount of baselines is worth researching. This letter proposes a novel missing data compensation approach in TomoSAR via matrix completion (MC), which is a technique of the low-rank matrix recovery from a subset of the matrix elements. In the proposed method, we first exploit MC to estimate the 2-D focused image data of unknown baselines, which are located on the uniform data grid without changing the length of elevation aperture. Based on the recovered data, we then recover the elevation reflectivity function by the spectral analysis (SA) method. Compared with the conventional SA technique, the proposed approach can achieve much higher elevation image quality, due to more available data offered by MC. The effectiveness of the proposed method is validated with the experimental results based on simulated and real airborne data.

This paper focus on the polarimetric synthetic aperture radar (SAR) tomography for forested areas based on compressive MUSIC. In the proposed method, full polarimetric SAR echo signal reflected from the imaging area is collected, the corresponding multiple measurement vector model is established according to the parameters of polarimetric channels, the wavelet basis is adopted for representing the sparse vertical structure of the imaging area, and finally, the backscattering coefficients of the area are reconstructed by compressive MUSIC algorithm. The necessary number of tracks for SAR tomography is reduced and the severity of spurious spikes is suppressed under the same measurement accuracy. Simulation results from the PolSARpro data validate the effectiveness.