SAR Imaging Method Research Articles

Approximate Observation Weighted ℓ2/3 SAR Imaging under Compressed Sensing.

Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted ℓ2/3-norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ℓq(q = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply ℓ2/3 regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method.

A GAN-based fast focusing method for circular SAR images

Circular Synthetic Aperture Radar(CSAR) imaging is vulnerable to perturbations in the atmosphere and various other elements that can lead to position offset errors in the antenna's phase center as well as induce motion errors. Traditional phase compensation methods that operate in the time domain, such as Auto-regressive Back-projection (ARBP), typically require computation on a direction-by-direction basis, which can result in the considerable expenditure of time and memory resources. To address these challenges, thispaperintroduces a novel approach for focusing on CSAR images. This method leverages the training of a Generative Adversarial Network (GAN) to directly achieve focus on CSAR sub-aperture images. Additionally, to counteract the network's tendency towards low-frequency preferences, the Auto-focus Frequency Loss (AFFL) is introduced. Moreover, to enhance the accuracy of focus position extraction, the Focus Position Feature Attention (FPFA) is proposed. These innovations, along with a new fusion strategy for the sub-aperture images post-focusing, have been experimentally validated, demonstrating significant improvements in the efficiency and accuracy of CSAR image focusing.

Dynamically Self-Training Open Set Domain Adaptation Classification Method for Heterogeneous SAR Image

Dynamically Self-Training Open Set Domain Adaptation Classification Method for Heterogeneous SAR Image

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

A Novel Category Discovery Method for SAR Images Based on an Improved UNO Framework

A Novel Category Discovery Method for SAR Images Based on an Improved UNO Framework

Enhanced One-Bit SAR Imaging Method Using Two-Level Structured Sparsity to Mitigate Adverse Effects of Sign Flips

Enhanced One-Bit SAR Imaging Method Using Two-Level Structured Sparsity to Mitigate Adverse Effects of Sign Flips

A two-stage semi-supervised object detection method for SAR images with missing labels based on meta pseudo-labels

Recently, deep learning has been applied to analyze satellite images. However, applying fully supervised object detection (FSOD) is impractical because it is challenging to detect and annotate objects that are relatively small in high-resolution satellite images. In addition, most data owned by public institutions, such as military reconnaissance videos, do not contain sufficient object class label information. Therefore, the application of semi-supervised objection detection (SSOD) is more practical. The SSOD performance is often determined by generated pseudo-labels. Therefore, this study proposes a 2-stage SSOD model to generate accurate pseudo-labels. In the first stage, annotations, including the location information of unlabeled data, are detected using a modified faster R-CNN model. In the second stage, pseudo-labels of objects are additionally suggested through the meta pseudo-label method. Finally, reliable pseudo-labels are generated by comparing and combining the pseudo-labels generated in each stage, improving the SSOD model performance. Extensive experiments were conducted using the xView3 dataset provided by the U.S. Defense Innovation Unit (DIU). The proposed method performed approximately 11% better than the FSOD model, which does not learn pseudo-labels for datasets with missing labels according to the evaluation metrics proposed by the DIU covering both object detection and classification performance.

Wide angle SAR imaging method based on hybrid representation

AbstractIn this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality.

Study of Sparse Terahertz Synthetic Aperture Imaging Algorithm

For the terahertz synthetic aperture near-field imaging problem, this paper proposes a SAR imaging method based on sparse reconstruction, which solves the problems of slow scanning speed, a large amount of data, and complicated calculations in the terahertz synthetic aperture imaging process. The targets in space are sparsely sampled and reconstructed using a compressed sensing reconstruction method. Simulation results show that high-quality target reconstruction can be achieved at low sampling conditions.

An Effective Multimodel Fusion Method for SAR and Optical Remote Sensing Images

An Effective Multimodel Fusion Method for SAR and Optical Remote Sensing Images

A Novel Airborne SAR Imaging Method Based on Modified Omega-K Algorithm

A Novel Airborne SAR Imaging Method Based on Modified Omega-K Algorithm

EDTRS: A Superpixel Generation Method for SAR Images Segmentation Based on Edge Detection and Texture Region Selection

The generation of superpixels is becoming a critical step in SAR image segmentation. However, most studies on superpixels only focused on clustering methods without considering multi-feature in SAR images. Generating superpixels for complex scenes is a challenging task. It is also time consuming and inconvenient to manually adjust the parameters to regularize the shapes of superpixels. To address these issues, we propose a new superpixel generation method for SAR images based on edge detection and texture region selection (EDTRS), which takes into account the different features of SAR images. Firstly, a Gaussian function is applied in the neighborhood of each pixel in eight directions, and a Sobel operator is used to determine the redefined region. Then, 2D entropy is introduced to adjust the edge map. Secondly, local outlier factor (LOF) detection is used to eliminate speckle-noise interference in SAR images. We judge whether the texture has periodicity and introduce an edge map to select the appropriate region and extract texture features for the target pixel. A gray-level co-occurrence matrix (GLCM) and principal component analysis (PCA) are combined to extract texture features. Finally, we use a novel approach to combine the features extracted, and the pixels are clustered by the K-means method. Experimental results with different SAR images show that the proposed method outperforms existing superpixel generation methods with an increase of 5–10% in accuracy and produces more regular shapes.

Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging.

Lq (0 < q ≤ 1) regularization has been confirmed effective when applied to sparse SAR imaging. However, the inaccuracies caused by motion errors in the observation model will lead to various degradations and defocus in the reconstructed image. For high-resolution and light-small SAR systems, the range-variant motion errors will decrease the accuracy of range cell migration correction (RCMC), and residual range cell migration (RCM) will exceed multiple range resolution cells and degrade the image quality substantially. Aiming at this problem, in this paper, a novel azimuth-range decoupled sparse SAR imaging method with coarse-to-fine range-variant motion errors and residual RCM correction method is proposed. First, a one-step motion compensation (MOCO) operator is proposed using the inertial navigation systems (INS)/global positioning systems (GPS) information, which can significantly reduce the residual RCM and improve the reconstruction accuracy. Second, a fine high-order phase-error correction method is performed to correct the range and cross-range-varying phase errors using a joint imaging and phase-error estimation scheme, which will further improve the image focusing quality. Experimental results indicate the effectiveness of the proposed method.

Enhanced SAR imaging using multiple plane spiral orbital angular momentum beams

We investigate the application of vortex beams carrying different orbital angular momentum (OAM) modes to SAR systems. For the traditional OAM-carrying wave, the inconsistent divergence angles exhibit that it is difficult to simultaneously illuminate the target with different OAM beams, resulting in limited echo energy or deterioration of the imaging capability. In this work, an enhanced SAR imaging method is proposed, which exploits plane spiral OAM (PS-OAM) to form a pencil beam and eliminates the above negative effects. First, the echo signal model for conventional vortex SAR is derived. Second, the feasibility of PS-OAM-based beamforming techniques for high-resolution SAR imaging is analyzed. Third, in light of the echo model of the proposed vortex SAR, an improved chirp scaling (CS) algorithm is provided by considering amplitude correction and phase compensation. Finally, the numerical simulations demonstrate the proposed method can achieve high-precision focusing processing in comparison with the traditional algorithm.

SAR Imaging Method for Moving Targets Based on Omega-k and Fourier Ptychographic Microscopy

The performance of traditional SAR imaging methods is limited by the system bandwidth, and the imaging results are easily affected by the target motion state. In the process of SAR imaging, the relative motion between the radar and the target will cause the nonlinear phase error of the echo signal, which makes the imaging quality worse. To solve this problem, we propose a high-resolution moving targets SAR imaging method based on Omega-k and Fourier Ptychographic Microscopy (FPM) phase retrieval technology, which realizes phase retrieval by alternating iteration between time domain and 2-D frequency domain. The proposed method is mainly divided into two steps. Firstly, reference function multiply and Stolt mapping are performed to realize echo preprocessing in the 2-D frequency domain, and then the phase error correction of the compensated echo spectrum is completed by FPM method. Several groups of simulation data and measured data are used for experimental analysis, and the results verify the effectiveness of the proposed method.

SAR Imaging Method for Moving Target With Azimuth Missing Data

To solve the problem of the moving target SAR imaging from an incomplete echo, we proposed a novel SAR imaging method for the moving target with the azimuth missing data (MTIm-AMD). Instead of directly reconstructing the moving target image using the sparse SAR imaging method, we estimate and reconstruct the non-cooperative moving target&#x0027;s complete echo from the azimuth incomplete echo based on its sparsity. At first, the non-cooperative moving target&#x0027;s motion parameters are estimated from the incomplete echo. Then, to ensure that the complete echo can be well reconstructed using the compressed sensing method, these parameters are exploited to design a phase compensation function. Finally, using the reconstructed data, the fine-focused moving target can be obtained via the traditional SAR imaging algorithm. The simulation and experiment data results verify the effectiveness of the proposed MTIm-AMD method and demonstrate that the moving target can be fine-imaging when the echo SNR is higher than <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>20 dB and the azimuth missing ratio (AMR) is less than 70&#x0025;. This implies that the proposed MTIm-AMD method achieves satisfactory robustness to echo SNR and can handle most AMR cases.

A 3-D SAR Imaging Method Based on Back Projection Algorithm

3D back projection (BP) algorithm is an imaging algorithm based on time domain echo data, which effectively solves the overlapping mask problem existing in 2D SAR. It can complete the imaging processing of echo signal under any geometry configuration, and has the advantages of high target focusing accuracy and high phase preservation. However, the high complexity and low efficiency of 3D BP imaging algorithm limit its application and development. In this paper, a 3d imaging method based on improved back projection algorithm is proposed. Aiming at the problem that existing imaging algorithms need 2D imaging first and then 3D imaging, an improved 3D BP algorithm is proposed to directly 3D imaging, which avoids 2d imaging processing. The proposed method simplifies the steps of the traditional 3D BP algorithm and improves the efficiency of the algorithm. The validity and effectiveness of the proposed method are verified by the 3d imaging results of simulated lattice targets.

Retraction Note to: Extreme rainfall response based on SAR imaging and home sports training effect

This paper establishes the value relationship model between the imaging performance of GEO bistatic SAR and bistatic geometry and proposes a design method of GEO bistatic SAR imaging method based on genetic algorithm. This method can automatically calculate the best imaging effect through multiple targets and objectively optimize the bistatic geometry configuration method, thus laying a theoretical foundation for high-resolution GEO imaging with bistatic SAR imaging. Accordingly, this study takes the response of Y City to extreme rainfall in 2017 as an example to study its rainfall characteristics, erosion disasters, and erosion causes, so as to evaluate the precipitation flow during rainstorm. Under the condition of gradient erosion in response to extreme rainfall, based on the difficulties of rainfall runoff depth and hydraulic parameters measurement in the Loess Plateau, and in order to prevent the dynamic characteristics of hydraulic erosion in the runoff area of the Loess Plateau, the unconventional scale phenomenon in the Loess Plateau was preliminarily studied. Finally, this paper introduces the research theme of the body and daily activities of the elderly women with mild disability after home exercise intervention and uses literature data method, questionnaire survey method, body measurement method, experimental method, and mathematical statistics method to select 60 elderly women with mild disability after home care. They were randomly divided into the experimental group with 30 people and the control group with the same number of people. In the experimental group, the elderly with mild disability were given a 12-week home exercise intervention experiment, and after the experiment, the indexes of physical fitness were compared with those before the experiment, so as to obtain the feedback of training effect and ability. Through the above experiments, this paper proves the reliability and effectiveness of home exercise intervention training on the improvement of the physical quality of the elderly.

A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram–Schmidt Transformation

To solve the problems such as obvious speckle noise and serious spectral distortion when existing fusion methods are applied to the fusion of optical and SAR images, this paper proposes a fusion method for optical and SAR images based on Dense-UGAN and Gram–Schmidt transformation. Firstly, dense connection with U-shaped network (Dense-UGAN) are used in GAN generator to deepen the network structure and obtain deeper source image information. Secondly, according to the particularity of SAR imaging mechanism, SGLCM loss for preserving SAR texture features and PSNR loss for reducing SAR speckle noise are introduced into the generator loss function. Meanwhile in order to keep more SAR image structure, SSIM loss is introduced to discriminator loss function to make the generated image retain more spatial features. In this way, the generated high-resolution image has both optical contour characteristics and SAR texture characteristics. Finally, the GS transformation of optical and generated image retains the necessary spectral properties. Experimental results show that the proposed method can well preserve the spectral information of optical images and texture information of SAR images, and also reduce the generation of speckle noise at the same time. The metrics are superior to other algorithms that currently perform well.

RETRACTED ARTICLE: Extreme rainfall response based on SAR imaging and home sports training effect

This paper establishes the value relationship model between the imaging performance of GEO bistatic SAR and bistatic geometry and proposes a design method of GEO bistatic SAR imaging method based on genetic algorithm. This method can automatically calculate the best imaging effect through multiple targets and objectively optimize the bistatic geometry configuration method, thus laying a theoretical foundation for high-resolution GEO imaging with bistatic SAR imaging. Accordingly, this study takes the response of Y City to extreme rainfall in 2017 as an example to study its rainfall characteristics, erosion disasters, and erosion causes, so as to evaluate the precipitation flow during rainstorm. Under the condition of gradient erosion in response to extreme rainfall, based on the difficulties of rainfall runoff depth and hydraulic parameters measurement in the Loess Plateau, and in order to prevent the dynamic characteristics of hydraulic erosion in the runoff area of the Loess Plateau, the unconventional scale phenomenon in the Loess Plateau was preliminarily studied. Finally, this paper introduces the research theme of the body and daily activities of the elderly women with mild disability after home exercise intervention and uses literature data method, questionnaire survey method, body measurement method, experimental method, and mathematical statistics method to select 60 elderly women with mild disability after home care. They were randomly divided into the experimental group with 30 people and the control group with the same number of people. In the experimental group, the elderly with mild disability were given a 12-week home exercise intervention experiment, and after the experiment, the indexes of physical fitness were compared with those before the experiment, so as to obtain the feedback of training effect and ability. Through the above experiments, this paper proves the reliability and effectiveness of home exercise intervention training on the improvement of the physical quality of the elderly.