Spiking Neural Networks (SNNs) have emerged as an essential paradigm for brain-inspired computing, achieving superior energy efficiency on neuromorphic hardware. However, as network scale increases, SNNs encounter growing challenges in deployment efficiency. While structured pruning provides a practical approach, existing methods typically rely on a dense-to-sparse training pattern, which incurs high computational costs during training and fails to exploit the efficiency of sparse computation from the outset. There is a lack of effective frameworks that enforce structured sparsity within a sparse-to-sparse training regime for SNNs. To bridge this gap, we propose the Channel Dual-phase Sparsity (C-DPSS) framework. This approach leverages Dynamic Sparse Training (DST) to enable efficient topology exploration while progressively enforcing hardware-friendly structured sparsity. Our methodology is grounded in the empirical observation that channel-level neuronal activity patterns stabilize earlier than synaptic weights under certain training regimes. Motivated by this temporal discrepancy, C-DPSS employs a dual-phase saliency metric during training. It defines an early exploration phase dominated by neuronal dynamics, and smoothly transitions to a late refinement phase driven by synaptic efficacy. Extensive experiments on both static and neuromorphic benchmarks demonstrate that C-DPSS provides a robust accuracy-efficiency trade-off. Notably, for VGG-16 on CIFAR-10, C-DPSS achieves 50% channel sparsity and reduces SOPs by 42.1% under S W = 0.90 and S C = 0.50 , with no observable accuracy degradation, showing a favorable accuracy–efficiency trade-off under structured channel pruning. We further examine the large-scale behavior of our approach through an ImageNet-1K feasibility study of sparse-to-sparse structured pruning. Our code is available at: https://github.com/JunLi0514/C-DPSS .

Azimuth ambiguity in azimuth multichannel system (AMCS) results from nonuniform sampling and degrades the synthetic aperture radar (SAR) imaging quality. In addition, the uniform pulse transmission pattern adopted in most AMCS is difficult to balance the azimuth resolution and range swath width performance. Furthermore, the design of pulse transmission pattern and reconstruction algorithm are often considered as two independent problems, which is difficult to achieve the optimal high-resolution and wide-swath (HRWS) imaging performance. In this article, we propose a joint optimization network to learn the nonuniform pulse transmission pattern and train reconstruction network parameters simultaneously, dubbed PTIR-Net. Specifically, we develope a nonuniform multichannel imaging model, consisting of the nonuniform approximate measurement operators with continuously defined pulse transmission time. To facilitate a more stable and generalized network, we introduce the implicit regularization learned by a model-based deep learning image reconstruction scheme to process the nonuniform sampling data. Extensive experiments on Gaofen-3 dataset show that the proposed network achieves better reconstruction performance in terms of both quantitative metrics and visual quality.

The multi-agent consensus equilibrium (MACE) mechanism possesses the notable capacity to incorporate multiple priors for SAR imaging and feature enhancement. We integrate the MACE mechanism with the combined dictionary (CD) regularization model and establish an optimized multi-feature sparse imaging method. The optimized method can effectively reduce storage and computational cost. While solving the parameter selection criteria of the proposed method, real data from a Ku-band SAR amounted on a Unmanned Aerial Vehicle is utilized to evaluate the performance.

Few-shot object detection (FSOD) aims to detect novel instances with only a limited number of labeled training samples, presenting a challenge that is particularly prominent in numerous remote sensing applications such as endangered species monitoring and disaster assessment. Existing FSOD methods for remote sensing images (RSIs) have achieved promising progress but remain constrained by the limited diversity of instances. To address this issue, we propose a novel framework that can leverage a diffusion model pretrained on large-scale natural images to synthesize diverse remote sensing instances, thereby improving the performance of few-shot object detectors. Instead of directly synthesizing complete remote sensing images, we first generate instance-level slices via a specialized slice-to-slice module, and then embed these slices into full-scale imagery for enhanced data augmentation. To further adapt diffusion models for remote sensing scenarios, we develop a class-agnostic image inversion module that can invert remote sensing instance slices into semantic space. Additionally, we introduce contrastive loss to semantically align the synthesized images with their corresponding classes. Experimental results show that our method has achieved an average performance improvement of 4.4% across multiple datasets and various approaches. Ablation experiments indicate that the elaborately designed inversion module can effectively enhance the performance of FSOD methods, and the semantic contrastive loss can further boost the performance.

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 (SAR) tomography (TomoSAR) is an advanced remote sensing technology that has the ability to acquire three-dimensional information of targets. To enhance target features, different reaularization terms, including L1norm, total variation norm, and morphology term, have been introduced into the TomoSAR inversion model. However, there is a lack of attention to establishing a TomoSAR imaging framework with a modular structure that provides flexibility in specifically enhancing different features. In this article, a novel 3D tomographic reconstruction framework based on plug-and-play (PnP) priors and Alternating Direction Method of Multipliers (ADMM) is proposed. The PnP-ADMM framework achieves flexibility through the selection of appropriate priors for the features of targets, leading to a trade-off between the performance of feature enhancement and computational complexity. Simulation and real data experiments verify the effectiveness of the proposed method in flexibly selecting priors for feature enhancement of specific targets.

The multiagent consensus equilibrium (MACE) mechanism, which generalizes the popular method plug-and-play (PnP)-alternating direction method of multipliers (ADMM) and composite regularization in computational sensing, possesses the notable capacity to incorporate multiple priors aspect of both regularization and PnP for improving image quality. In this work, a flexible synthetic aperture radar (SAR) image reconstruction method based on MACE is proposed to integrate multiple regularization and PnP priors for various features enhancement. The partial-update approach and Mann iteration methods are implemented to increase the computational efficiency of the MACE-based SAR image reconstruction algorithm. A thorough analysis of the proposed algorithm’s convergence and computational complexity is provided. High-quality SAR images necessitate low ambiguity, high target-to-background ratio (TBR), and low coherent speckle. We therefore demonstratively integrate regularization and PnP priors for azimuth ambiguity suppression, sparsity inducing, multiple features enhancement, and despeckling. The proposed method’s performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data.

Synthetic aperture radar (SAR) tomography (TomoSAR) retrieves three-dimensional (3-D) information from multiple SAR images, effectively addresses the layover problem, and has become pivotal in urban mapping. Unmanned aerial vehicle (UAV) has gained popularity as a TomoSAR platform, offering distinct advantages such as the ability to achieve 3-D imaging in a single flight, cost-effectiveness, rapid deployment, and flexible trajectory planning. The evolution of compressed sensing (CS) has led to the widespread adoption of sparse reconstruction techniques in TomoSAR signal processing, with a focus on ℓ1norm regularization and other grid-based CS methods. However, the discretization of illuminated scene along elevation introduces modeling errors, resulting in reduced reconstruction accuracy, known as the “off-grid" effect. Recent advancements have introduced gridless CS algorithms to mitigate this issue. This paper presents an innovative gridless 3-D imaging framework tailored for UAV-borne TomoSAR. Capitalizing on the pulse repetition frequency (PRF) redundancy inherent in slow UAV platforms, a multiple measurement vectors (MMV) model is constructed to enhance noise immunity without compromising azimuth-range resolution. Given the sparsely placed array elements due to mounting platform constraints, an atomic norm soft thresholding algorithm is proposed for partially observed MMV, offering gridless reconstruction capability and super-resolution. An efficient alternative optimization algorithm is also employed to enhance computational efficiency. Validation of the proposed framework is achieved through computer simulations and flight experiments, affirming its efficacy in UAV-borne TomoSAR applications.

The current synthetic aperture radar (SAR) images with ultrahigh-resolution provide detailed structures of the urban areas. Utilizing stacks of ultrahigh-resolution SAR images acquired with different view angles, tomographic SAR (TomoSAR) becomes an advanced technique to retrieve 3-D spatial information of the detailed structures which presents the efficient density in the point clouds. The technique is a sparse reconstruction problem indeed and can be solved by compressive sensing (CS) algorithms. However, conventional CS algorithms process the pixel independently and the detailed structures of the targets are easily lost followed by the sparsity constraints. In this article, we apply morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization enhances the detailed 3-D structural properties of targets, which can shrink the concatenations caused by outliers in the iterative reconstruction. As for the optimization algorithm for tomographic inversion, we apply the framework of the alternating direction method of multipliers (ADMMs), where the Bregman iteration is adopted for solving the subproblem with morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and guarantee the detection rate, leading to excellent correctness and completeness.

With the abomination of time-consuming or even impractical large-scale labeling, few-shot object detection (FSOD) based on natural scenes has attracted extensive attention. However, directly migrating FSOD methods designed for natural images to large-size remote sensing images (RSIs) still remains challenges. 1) Labels of novel instances within the base dataset are inconsistently assigned between the base training and the few-shot fine-tuning stage, which confuses the detector and leads to significant performance degradation over novel classes. 2) The region proposal network (RPN) of detectors cannot provide sufficient high-quality proposals for remote sensing objects with various aspect ratios and irregular shapes, leading to decreased detection performance. To tackle these issues, we specify a novel few-shot object detector for RSIs, to avoid the significant performance degradation caused by inconsistent labeling assignments, as well as efficiently leveraging the novel instances that existed in the base dataset. Furthermore, the proposed detector utilizes a coarse-to-fine regression method with an enhanced feature extractor called Gradual RPN to improve the recall of RPN. Experiments on a newly constructed few-shot detection benchmark show that our approach improves the mAP of novel classes by up to 8.4% and the average recall of RPN by up to 12.3%. The source code is available at here.

Sparse signal processing techniques, such as compressed sensing (CS), are commonly used in tomographic synthetic aperture radar (TomoSAR) imaging due to the sparsity present in the elevation direction. However, classical CS methods, such as$\ell _{1}$-norm regularization and orthogonal matching pursuit (OMP), suffer from the off-grid effect. Specifically, they discretize the elevation axis into multiple grids and assume that scatterers are located precisely on the grids, leading to reconstruction results that deviate from the true heights of the scatterers. Although gridless CS methods, such as atomic norm minimization (ANM), have achieved gridless reconstruction in specific scenarios, they face challenges such as the requirement of uniformly distributed baselines and large computational cost. In this article, we propose a gridless CS method based on the alternate descent conditional gradient (ADCG) kernel for TomoSAR inversion and compare it with ANM, iterative soft thresholding (IST), and OMP. We show through numerical simulations and experimental results that our proposed method is applicable not only to scenarios with uniformly distributed baselines but also to scenarios with nonuniformly distributed baselines, and it resolves the off-grid effect in both cases. Finally, we demonstrate the effectiveness of our proposed method by implementing gridless reconstruction using an actual dataset of urban buildings in Yuncheng.

Highly-squinted synthetic aperture radar (SAR) echo has the characteristic of severe range-azimuth coupling, requiring specialized imaging algorithms. Applications of compressed sensing in SAR imaging can effectively improve the resolution and other indicators. However, inaccurate manual parameters can affect the algorithm output. This article proposes an improved alternating direction method of multipliers (ADMM) for solving sparse reconstruction models under highly-squinted conditions. By adaptively adjusting the penalty parameter in ADMM via hyper-gradient descent (HD), the problem caused by inaccurate manual parameter is solved. Compared with matched filtering methods and other optimization methods, this method can suppress noise and speed up convergence. The effectiveness of the proposed method can be validated through the approximate observation of both simulated scenes and real scenes captured by the GF-3 SAR satellite.

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3-D spatial information. However, decorrelation effects degrade the quality of interferometric phases, resulting in errors in the reconstruction. In this paper, we propose a denoising method based on the unsupervised convolution neural network (CNN) with a loss function combining the deterministic descriptive regularization and total variation (TV) term. It can improve both the accuracy and completeness of the reconstructed 3-D point clouds, which is verified by experiments on simulated and real SAR images.

Synthetic aperture radar (SAR) images have been widely used in water body information extraction. However, SAR images suffer from speckles and the additive noise, which affect the performance of automatic information extraction. Thus, we propose the nonconvex-nonlocal total variation (NLTV) regularization to suppress speckles and the additive noise, and improve the performance of water body information extraction using the enhanced images. Experiments using Qilu-1 (QL-1) SAR data verify the effectiveness of the method.

High resolution and high quality are now the requirements in synthetic aperture radar (SAR) research. The sliding spotlight mode can obtain high azimuth resolution because of its large azimuth bandwidth. Group sparse penalty can effectively suppress azimuth ambiguities to improve image quality. Generalized mini-max concave (GMC) penalty is a kind of nonconvex penalty, which is widely used in SAR imaging. In this paper, a novel sliding spotlight SAR imaging method based on group sparsity and nonconvex regularization is proposed. Compared with matched filtering method, the proposed method can suppress noise and azimuth ambiguities. Both simulations and Qilu-1(QL-1) real SAR data experiments verify the effectiveness of the proposed method.

Abstract With the rise of the mobile Internet, APP treatment models for autism have also emerged. In China, there are not many applications of health education for children with autism. Its functions are mainly focused on prevention and improvement of knowledge. Many behavioral therapies include applying pressure to the invisible external pressure of autism to achieve appropriate stimulation and correction. This has brought great injustice to the wishes of the children. When children with autism strongly oppose this unfair behavior, it may lead to other extreme behaviors in children with autism. This article adopts the method of letting children with autism actually apply this APP for comprehensive intervention treatment, and let this APP combine with their daily courses: comprehensive sports training, speech training, cognitive training, game training, self‐care training. For courses such as art training and computer games, the children were evaluated for the first time before training, and then evaluated every 2 months. Data were collected to compare whether the children's background factors had a significant impact on the rehabilitation effect of comprehensive interventions. The results of the experiment proved that in the five assessments of perception, gross motor, fine motor, language and communication, cognition, the children had statistically significant differences in the three scores (P < 0.05). This also shows from the side that our APP has a certain effect on children with autism, and is of great strategic significance for adjuvant treatment of autism.

The azimuth multichannel SAR is competent to achieve high-resolution and wide-swath (HRWS) imaging. For some spaceborne multichannel SAR systems, the pulse repetition frequency (PRF) of each channel at some beam positions is less than that of uniform sampling, hence leading to the azimuth ambiguities in the recovered images. In this paper, a novel azimuth ambiguity suppression method for multichannel SAR imaging based on$L_{2,q}$regularization$(0 < q\leq 1)$is proposed. First, by analyzing the reasons of azimuth ambiguities in multichannel SAR, we establish the imaging model different from that in single-channel SAR system. Second, we extend the$L_{2,q}$regularization from single-channel SAR system to multichannel SAR system and develop the proposed method. Finally, we demonstrate the effectiveness of the proposed method for non-sparse scenarios. Simulations and Gaofen-3 real data experiments are carried out to verify the validity of proposed method.

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.

Sparse signal processing theory has been applied to SAR imaging. The estimation of sparsity is crucial for sparse SAR imaging. But the true value of sparsity is unknown. Adaptive parameter estimation for sparse SAR imaging can achieved by the automatic regularization parameter estimating methods. However, these methods are deduced based on measurement matrix, which will cause huge computational and memory costs. Also, the adaptive estimated sparsity is often greater than the true value due the noise and sidelobes. In this paper, we propose improved adaptive parameter estimation method for sparse SAR imaging. The complex-image-based sparse SAR imaging is adopted to pre-estimate the parameter. Then, azimuth-range decouple operators are introduced into parameter estimation method. Simulation and real data experimental results show the effectiveness of the proposed method.

In this paper, basis pursuit denoising (BPDN) is applied to extract three-dimensional scattering centers of complex radar targets. Since the distributions of scattering centers are usually sparse in the high-frequency optics region, the valid backscattering coefficients of radar targets can be obtained with the undersampled measurements in azimuth and elevation. It significantly reduces the time for measuring echo signals, which promotes the efficiency of radar systems. However, due to the great computation load and memory cost caused by the matrix-vector products, most of the sparse reconstruction algorithms are not suitable for the process of scattering center extraction. To solve this problem, we derive accelerated operators on the basis of support set and filtered backprojection. By combining the sparse solver SPGL1 with the proposed operators, the new algorithm not only reduces the consumption of system resources, but also estimates the backscattering coefficients accurately. Additionally, the experimental results and analysis demonstrate that the proposed technique possesses high data compression ratio and small radar cross section (RCS) reconstruction error.

Sparse signal processing has been applied in synthetic-aperture radar (SAR) imaging. As a typical sparse reconstruction model, L1regularization often underestimates the intensities of the targets. The estimated radar cross section (RCS) is related to the pixel intensity. Thus, the linear relationship between the targets' intensities cannot kept. The underestimation will also cause radiometric errors and affect the quantitative use of the SAR data. In this letter, we present a SAR imaging method based on generalized minimax concave (GMC) penalty. GMC is a nonconvex penalty and its cost function is convex. GMC can avoid the underestimation of pixel intensity. In the iteration, the azimuth-range decouple operators are used to avoid the huge memory and computational costs. The performance of the proposed method is verified using real data.

Multi-channel sliding spotlight SAR can achieve high-resolution and wide-swath imaging. The sparse reconstruction algorithm can improve the quality of imaging. By applying the sparse reconstruction algorithm to multichannel sliding spotlight SAR imaging, azimuth ambiguities, noise and clutter can be suppressed effectively. In this paper, ll regularization based multi-channel sliding spotlight SAR imaging method is proposed. The proposed method combines the DPCA imaging operators with the l1 regularization scheme to solve the nonuniform sampling and azimuth ambiguities problem. The proposed method can suppress azimuth ambiguities more effectively than the reconstruction filter algorithm based DPCA technology in the case of a lower PRF. The experiment results verify the effectiveness of the proposed method.

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.

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.

Due to the great computation load and memory cost of the matrix-vector multiplication, the sparse reconstruction algorithms are severely limited in the applications of radar imaging with real data. In order to solve this problem, we construct a backprojection-based range-azimuth decoupled operator (BP-RADOp) and combine the complex approximate message passing algorithm (CAMP) with it. We call this algorithm BP-CAMP in this letter. Since BP-RADOp retains the merits of the backprojection method entirely (i.e., perfect motion compensation for any flight path, precise focus for arbitrarily wide bandwidths and integration angles, low artifact levels, unlimited scene size, and strictly local processing), it has universal applicability in comparison with the other decoupled operators deduced from the fast Fourier transform-based image formation algorithms. The theoretical analysis indicates when BP-CAMP and CAMP are both used to reconstruct large-scale observed scenes; the former has lower computation load and memory cost than the latter. Meanwhile, it is demonstrated that BP-CAMP achieves high-quality synthetic aperture radar imaging with undersampled echo data, and it is as robust as CAMP to additive noise by the simulations and real data processing.

The displaced phase center antenna (DPCA) technique is a classical method for achieving high-resolution wide-swath synthetic aperture radar (SAR) imaging. For optimum performance, the pulse repetition frequency (PRF) of DPCA SAR systems should satisfy the azimuth uniform sampling condition as far as possible. However, this rigid PRF selection may conflict with the timing diagram for some incidence angles, which usually results in a nonuniform sampling of the synthetic aperture. According to the sparse signal processing theory, this letter proposes a novel DPCA imaging algorithm for the nonuniform displaced phase center sampling. By combining the DPCA data processing operator based on a filter bank with the ℓqregularization scheme, the algorithm can efficiently recover the backscattering coefficients of the observed scene. The experimental results have shown that it is capable of resolving ambiguity and suppressing clutter effectively and is meanwhile insensitive to additive noise.

Recently, the sparse reconstruction algorithms (SRAs) based on compressive sensing (CS) have been applied in the fields of synthetic aperture radar (SAR) imaging and show plenty of potential advantages. However, due to the great computational complexity and memory cost caused by matrix-vector multiplications, most of these algorithms are not suitable to reconstruct large-scale observed scenes. To solve this problem, we construct a backprojection based imaging operator, and introduce it to the complex approximate message passing algorithm (CAMP). The new image formation algorithm is called BP-CAMP in this paper. Compared with the approximated observation methods deduced from the FFT-based imaging technology, BP-CAMP is not limited by observation models of the radar and motion modes of the platform, and it therefore possesses universal applicability. By the simulations and real data processing, the experimental results show that BP-CAMP has lower computational complexity and memory cost than CAMP, and also achieves SAR imaging with under-sampled echo data.

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.

Compressed sensing (CS) techniques can reduce the sampling rates required in synthetic aperture radar (SAR). However, it is difficult to use the restricted isometry property to theoretically analyze the performance. Therefore, in this letter, information theory is applied to set necessary bounds on sampling ratios in CS-based SAR imaging. The system is viewed as a multi-input/multi-output (MIMO) channel, with information capacity quantified for a given measurement matrix and signal-to-noise ratio (SNR). According to the source-channel coding theorem, the lower bound of the sampling ratios is derived in terms of sparsity ratio, SNR, bandwidth, and radar pulse duration. Simulation studies are performed to test and analyze the information-theoretical bounds.

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.

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.

In this paper, we introduce a total variation (TV) regularization model for SAR image despeckling. A dual formulation based adaptive total variation (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV regularization based image restoration model has a good performance in preserving image sharpness and edges while removing noises and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. An evaluation index is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of a pixel with different sizes. Experimental results show that the proposed method can effectively suppress SAR image speckles without compromise the edge sharpness of image features according to both subjective visual examination and objective evaluation indices of image quality.

Range ambiguity in synthetic aperture radar (SAR) imaging primarily arises from scattered energy of bright targets outside the interested region. So to reduce the ambiguity, we need to identify these targets additionally, which yields an ill-posed problem. To find a feasible solution where the range ambiguity can be sufficiently reduced, we propose in this paper a new method using compressed sensing, a theory which tells when sparse signal can be reconstruct from undetermined linear system, by observing that the recognizable targets are approximately sparse in the ambiguous range zones. Therefore, it is possible to reconstruct the main region and identify the ambiguous targets simultaneously. The simulation results demonstrate the validation of the proposed method.

In this paper, we present the implementation of Iterative Shrinkage-thresholding Algorithm (ISTA) based on Graphic processing unit (GPU) parallel computation for sparse microwave imaging. First we introduce the theory of sparse microwave imaging and the mathematical model of Lq-norm regularization. Then taking the fast speed advantage of GPU on large-scale computation, we implement the ISTA with parallel computation via CUDA and apply it into sparse microwave imaging. The experiment simulations show that GPU has the same ability in signal reconstruction as CPU, which has less execution time and higher efficiency.

The displaced phase center antenna (DPCA) synthetic aperture radar (SAR) has the potential to achieve high azimuth resolution and wide swath. Its pulse repletion frequency (PRF) has to be selected such that SAR platform moves just one half of its total antenna length between subsequent radar pulses. If this condition is not satisfied, there will be nonuniform sampling in azimuth and azimuth ambiguities will appear when traditional imaging algorithms based on matched filter are used. We propose an innovative imaging algorithm based on compressed sensing (CS) which can reconstruct the scene well even though this rigid condition is not satisfied.

In this paper, a novel synthetic aperture radar (SAR) imaging method based on L1/2regularization is proposed. Our method implements SAR imaging from compressed measurements with high resolution, enhanced features, reduced sidelobes and suppressed artifacts. Real SAR data experiments are implemented to demonstrate the outperformance of our method. The experiment results demonstrate that our method needs far below the traditional Nyquist rate to guarantee successful imaging. Compared to the prevalent L1regularization-based methods, there is a significant reduction of the sampling rate for SAR imaging. The sampling rate used by our method is about half of the L1regularization-based methods in the real SAR data experiments.

Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments.

This paper analyses ambiguity suppression caused by multiple-input multiple-output (MIMO) SAR azimuth nonuniform samplings. Two methods are analyzed: azimuth spectrum reconstruction algorithm and minimum mean square error (MMSE) imaging algorithm. The azimuth spectrum reconstruction algorithm can reconstruct the scene fine resolution, while the nonideal orthogonality of multi-channel encoding waveforms causes azimuth ambiguous in SAR imaging. The MMSE imaging algorithm can perfectly reconstruct, while it requires high SNR.