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Synthesis of complex-valued InSAR data with a multi-task convolutional neural network

ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2025年 220卷 192-206页

作者： Sibler, Philipp Sica, Francescopaolo Schmitt, Michael Univ Bundeswehr Munich Dept Aerosp Engn Werner Heisenberg Weg 39 D-85577 Neubiberg Germany Hensoldt Sensors GmbH Graf Von Soden Str D-88090 Immenstaad Germany

Simulated remote sensing images bear great potential for many applications in the field of Earth observation. They can be used as controlled testbed for the development of signal and image processing algorithms or can provide a means to get an impression of the potential of new sensor concepts. With the rise of deep learning, the synthesis of artificial remote sensing images by means of deep neural networks has become a hot research topic. While the generation of optical data is relatively straightforward, as it can rely on the use of established models from the computer vision community, the generation of synthetic aperture radar (SAR) data until now is still largely restricted to intensity images since the processing of complex-valued numbers by conventional neural networks poses significant challenges. With this work, we propose to circumvent these challenges by decomposing SAR interferograms into real-valued components. These components are then simultaneously synthesized by different branches of a multi-branch encoder-decoder network architecture. In the end, these real-valued components can be combined again into the final, complex-valued interferogram. Moreover, the effect of speckle and interferometric phase noise is replicated and applied to the synthesized interferometric data. Experimental results on both medium-resolution C-band repeat-pass SAR data and high-resolution X-band single-pass SAR data, demonstrate the general feasibility of the approach.

关键词： synthetic aperture radar (SAR) Deep learning Multitask learning image synthesis SAR interferometry (InSAR)

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Sparse aperture Inverse synthetic aperture radar Imaging for Maneuvering Targets With Migration Through Resolution Cells Correction

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 4446-4472页

作者： Yang, Jiaxing Wang, Yong Harbin Inst Technol Res Inst Elect Engn Technol Harbin 150001 Peoples R China

Due to the nonstationarity of the echo, it is hard to form a well-focused sparse aperture inverse synthetic aperture radar (SA-ISAR) image for maneuvering targets by traditional algorithms. In this article, two algorithms are proposed to achieve the translational motion compensation and SA-ISAR imaging with migration through resolution cells (MTRC) correction for maneuvering targets respectively. First, with the stretch processing generated extra complex phase terms incorporated into the analysis, a modified parametric translational motion compensation algorithm is proposed to achieve the translational motion compensation. The translational parameters are estimated by optimizing the image entropy of the range-Doppler based ISAR image. Second, a novel SA-ISAR imaging framework with MTRC correction is proposed. After translational motion compensation, the nonstationary echo is intercepted by the short time window to extract the stationary echo. The missing data iterative adaptive approach-based random missing signal recovery and the interpolation-based MTRC correction are successively applied to the stationary echo. Subsequently, the iterative adaptive approach-based azimuth compression is applied in the framework to the valid MTRC-corrected range profile to form the focused ISAR image. Finally, the experimental results validate the effectiveness of the two proposed algorithms.

关键词： Inverse synthetic aperture radar (ISAR) imaging migration through resolution cells (MTRC) missing data iterative adaptive approach (MIAA) translational motion compensation Inverse synthetic aperture radar (ISAR) imaging migration through resolution cells (MTRC) missing data iterative adaptive approach (MIAA) translational motion compensation

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Ultrahigh Resolution ISAR Imaging of a Target With Unknown and Complex Motion Using SA-NCS Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 3671-3685页

作者： Gao, Yuexin Yu, Hanwen Xue, Min Chen, Jianlai Xidian Univ Sch Aerosp Sci & Technol Xian 710071 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 71007 Peoples R China Cent South Univ Sch Automat Changsha 410083 Peoples R China

In this manuscript, a novel inverse synthetic aperture radar (ISAR) imaging method for a large target under ultrahigh resolution condition is proposed. Certain problems in the existing ISAR imaging algorithms for ultrahigh resolution imagery are discussed. The problems caused by Keystone Transform under the condition of large bandwidth and long coherent processing interval are analyzed. Moreover, various means of focusing the signals with polynomial phase are analyzed as well. In order to solve these problems, the sub-aperture nonlinear chirp scaling (NCS) algorithm is presented. This algorithm divides the data in range-frequency/azimuth-time domain into several blocks. In each block, a modified NCS algorithm is applied to estimate the rotation parameters. The modified NCS algorithm takes the residual phase errors arising from NCS processing into consideration. It can achieve a better-focusing quality without bringing about too much additional computation burden. The proposed method is applied to acquired data, whose processing results are much better in comparison with the certain algorithm. These experiments validate the effectiveness of the proposed algorithm.

关键词： Imaging radar imaging image resolution Transforms Bandwidth signal resolution radar Polynomials Focusing Accuracy Inverse synthetic aperture radar (ISAR) modified nonlinear chirp scaling (NCS) sub-aperture ultrahigh resolution imaging

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An Efficient Parameter Estimation and Imaging Approach for Ground Maneuvering Targets by Mixed Symmetric Function in SAR imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Ma, Jingtao Xia, Xiang-Gen Huang, Penghui Wang, Lingyu Xia, Lang Deng, Yunkai Liao, Guisheng Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 201418 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Space Microwave Remote Sensing Syst Dept Beijing 100094 Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Ground moving target focusing performance and processing efficiency are two important metrics in the synthetic aperture radar (SAR) system. Owing to the complex nonstationary phases caused by target motions, ground maneuvering targets are commonly smeared and distorted in an SAR image. To realize the ground maneuvering target imaging, exhaustive high-order parameter searching and optimization processing are usually needed to achieve the optimal processing performance. However, the large computational resource consumption may limit their real-time processing capabilities. To deal with this issue, an efficient SAR imaging approach for ground maneuvering targets is proposed by adopting the mixed symmetric function (MSF) transform. After performing the multichannel SAR clutter rejection and target initial detection by applying the constant false alarm rate (CFAR) technique, the linear envelop shift of detected ground target is eliminated by using the well-known Keystone transform (KT). Then, an MSF is constructed to rectify the range curvature and relieve the Doppler broadening after carrying out the nonuniform fast Fourier transform (NUFFT) corresponding to the second-order time variable. After removing the second-order Doppler spreading influence, the quadratic chirp parameter is estimated via the NUFFT after the symmetrical correlation processing. Finally, after accomplishing the motion compensation, the ground maneuvering target can be well focused and relocated in the SAR image. Compared with the traditional approaches, the proposed approach not only realizes the high-precision Doppler parameter estimation, but is also computationally efficient without grid searching procedure. Numerical simulation and real-measured SAR experiment results are depicted to verify the correctness and effectiveness of the proposed approach.

关键词： Clutter synthetic aperture radar Doppler effect radar imaging Focusing Parameter estimation Imaging Azimuth Transforms radar polarimetry Ground maneuvering target imaging high efficiency high-order Doppler parameter estimation mixed symmetric function (MSF) synthetic aperture radar (SAR)

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Interchannel Antenna Pattern Correction for Azimuth Digital Beamforming in Airborne SAR

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Castillo, Juan Pablo Navarro Scheiber, Rolf Jaeger, Marc Moreira, Alberto German Aerosp Ctr DLR Microwaves & Radar Inst D-82234 Wessling Germany

High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.

关键词： Antennas Azimuth synthetic aperture radar Reconstruction algorithms Receiving antennas image reconstruction Antenna radiation patterns Filters Doppler effect Spaceborne radar Airborne synthetic aperture radar (SAR) antenna pattern azimuth SAR image reconstruction digital beamforming (DBF) range block processing

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Modified Time-Domain Backprojection Algorithm for SAR Frequency-Domain Autofocus

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Liu, Yanqi Fan, Jixia Tao, Manyi Shi, Tianyue Mao, Xinhua Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 211106 Peoples R China Shanghai Acad Spaceflight Technol Shanghai 201109 Peoples R China Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China Nanjing Univ Aeronaut & Astronaut Shenzhen Res Inst Shenzhen 518063 Peoples R China

The backprojection (BP) algorithm is a precise time-domain technique in synthetic aperture radar (SAR) imaging, known for its robustness in scenarios where frequency-domain algorithms struggle, such as nonlinear flight paths, high-resolution demands, or large imaging swaths. However, the unknown spectral structures of BP images pose significant challenges for frequency-domain autofocus algorithms, including phase gradient autofocus (PGA). This article introduces a novel interpretation of the BP algorithm, and then compares the image spectra between BP and the polar format algorithm (PFA). Our findings indicate that BP image's spectrum suffers from severe range ambiguity and spectral misalignment, which is the key reason why BP images cannot be directly applied to PGA. To address these issues, we first improve the postprocessing method, and then propose a modified BP algorithm. Both the approaches reconstruct the signal spectrum, thereby facilitating the direct application of PGA. Compared with the postprocessing method, the proposed algorithm is more efficient. Simulation and real data experiments validate the feasibility and efficacy of the proposed method, demonstrating the proposed algorithm's suitability for autofocusing in the frequency domain.

关键词： radar imaging Electronics packaging Frequency-domain analysis Imaging History signal processing algorithms Fourier transforms radar polarimetry Azimuth synthetic aperture radar Backprojection (BP) frequency-domain autofocus spectrum analysis synthetic aperture radar (SAR)

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SOLSTM: Multisource Information Fusion Semantic Segmentation Network Based on SAR-OPT Matching Attention and Long Short-Term Memory Network

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Chang, Hao Fu, Xiongjun Guo, Kunyi Dong, Jian Guan, Jialin Liu, Chuyi Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Beijing Inst Remote Sensing Equipment Beijing 100854 Peoples R China

With the significant advancements in deep learning technology and the substantial improvement in remote sensing image resolution, remote sensing semantic segmentation has garnered widespread attention. synthetic aperture radar (SAR) and optical images are the primary sources of remote sensing data, offering complementary information. SAR images can capture surface information even under cloud cover and at night, whereas optical images provide higher resolution in clear weather conditions. Deep learning-based feature fusion methods can effectively integrate multisource information to obtain more comprehensive surface data. However, there are significant spatiotemporal differences in multisource information, making it challenging to select and extract the most discriminative features for segmentation tasks. To address this, we propose a lightweight and efficient fusion semantic segmentation network, SOLSTM, which mixes SAR and optical images as inputs and performs cyclic cross-fusion to establish a new network paradigm. To tackle multisource data heterogeneity, we introduce SAR-OPT matching attention, which aggregates multisource image features by adaptively adjusting fusion weights, thereby achieving comprehensive perception of feature channels and contextual information. Additionally, to mitigate the high computational complexity of processing multidimensional data, we introduce the mLSTM block, which employs linear operations to mine global contextual information in fused images, thus reducing computational complexity and enhancing image segmentation performance. Experiments on the WHU-OPT-SAR dataset show that SOLSTM has excellent performance, achieving up to 52.9 mIoU and outperforming single source image segmentation, verifying the effective fusion of OPT-SAR.

关键词： Optical sensors Optical imaging Semantics remote sensing synthetic aperture radar Feature extraction Adaptive optics Semantic segmentation radar polarimetry Optical reflection Lightweight network multisource fusion remote sensing semantic segmentation synthetic aperture radar (SAR) terrain classification

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synthetic aperture radar processing Using Flexible and Seamless Factorized Back-Projection

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remote sensing 2025年第6期17卷 1046-1046页

作者： Polisano, Mattia Giovanni Manzoni, Marco Tebaldini, Stefano Politecn Milan Dept Elect Informat & Bioengn DEIB I-20133 Milan Italy

This paper describes a flexible and seamless processor for Unmanned Aerial Vehicle (UAV)-borne synthetic aperture radar (SAR) imagery. When designing a focusing algorithm for large-scale and high-resolution SAR images, efficiency and accuracy are two mandatory aspects to consider. The proposed processing scheme is based on a modified version of Fast Factorized Back-Projection (FFBP), in which the factorization procedure is interrupted on the basis of a computational cost analysis to reduce the number of complex operations at its minimum. The algorithm gains efficiency in the case of low-altitude platforms, where there are significant variations in azimuth resolution, but not in the case of conventional airborne missions, where the azimuth resolution can be considered constant in the swath. The algorithm's performance is derived by assessing the number of complex operations required to focus an SAR image. Two scenarios are tackled in a numerical simulation: a UAV-borne SAR with a short synthetic aperture and a wide field of view, referred to as the ground-based-like (GBL) scenario, and a classical stripmap scenario. In both cases, we consider mono-static and bi-static radar configurations. The results of the numerical simulations show that the proposed algorithm outperforms FFBP in the stripmap scenario while achieving the same performance as FFBP in the GBL scenario. In addition, the algorithm is validated thanks to an experimental UAV-borne SAR campaign in the X-band.

关键词： synthetic aperture radar (SAR) Unmanned Aerial Vehicles (UAV) UAV-borne SAR UAV-borne SAR focusing Fast Factorized Back-Projection Time-Domain Back-Projection (TDBP)

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The Bright Feature Transform for Prominent Point Scatterer Detection and Tone Mapping

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remote sensing 2025年第6期17卷 1037-1037页

作者： Vetaw, Gregory D. Jayasuriya, Suren Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

Detecting bright point scatterers plays an important role in assessing the quality of many sonar, radar, and medical ultrasound imaging systems, especially for characterizing the resolution. Traditionally, prominent scatterers, also known as coherent scatterers, are usually detected by employing thresholding techniques alongside statistical measures in the detection processing chain. However, these methods can perform poorly in detecting point-like scatterers in relatively high levels of speckle background and can distort the structure of the scatterer when visualized. This paper introduces a fast image-processing method to visually identify and detect point scatterers in synthetic aperture imagery using the bright feature transform (BFT). The BFT is analytic, computationally inexpensive, and requires no thresholding or parameter tuning. We derive this method by analyzing an ideal point scatterer's response with respect to pixel intensity and contrast around neighboring pixels and non-adjacent pixels. We show that this method preserves the general structure and the width of the bright scatterer while performing tone mapping, which can then be used for downstream image characterization and analysis. We then modify the BFT to present a difference of trigonometric functions to mitigate speckle scatterers and other random noise sources found in the imagery. We evaluate the performance of our methods on simulated and real synthetic aperture sonar and radar images, and show qualitative results on how the methods perform tone mapping on reconstructed input imagery in such a way to highlight the bright scatterer, which is insensitive to seafloor textures and high speckle noise levels.

关键词： point scatterer synthetic aperture radar synthetic aperture sonar

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GANFlow: A Hybrid Model for SAR image Target Open-Set Recognition Based on GAN and the Flow-Based Module

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2025年 18卷 7083-7099页

作者： Qin, Jikai Han, Jiusheng Liu, Zheng Ran, Lei Xie, Rong Yeo, Tat-Soon Air Force Engn Univ Xian 710051 Peoples R China Xidian Univ Guangzhou Inst Technol Guangzhou 510555 Peoples R China Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Natl Univ Singapore Dept Elect Engn Singapore 119077 Singapore

Most synthetic aperture radar (SAR) automatic target recognition (ATR) methods can achieve good recognition results only under the closed-set assumption. However, in practical applications, ATR models are often exposed to open environments, the general closed-set method may misclassify unknown categories as known categories, which is not reasonable. To tackle this issue, this article proposes an end-to-end hybrid model for SAR image open-set recognition (OSR), named GANFlow, which combines a generative adversarial network (GAN) with a flow-based module. The GANFlow achieves accurate classification of known categories and effective rejection of unknown categories. In this model, a classifiable convolution GAN is first designed to complete the training of the feature extraction module and classifier. Through adversarial training, the generated images enrich the training samples, which improves the ability of feature extraction and classification of the discriminator. Then, to find the difference in the probability density distribution of the extracted features, a flow-based module is adopted. Also, the features avoid interference from irrelevant background information in SAR images. Furthermore, by establishing an appropriate threshold, unknown categories can be efficiently rejected. Finally, the outputs of the classifier and the flow-based module are combined to complete the OSR of the SAR image target. The experimental results on the MSTAR and OpenSARShip public-measured datasets verify the robustness and generalization of the proposed method.

关键词： radar polarimetry Training Feature extraction Generative adversarial networks Target recognition image recognition Data models Accuracy synthetic aperture radar Support vector machines Automatic target recognition (ATR) flow generative adversarial network (GAN) open-set recognition (OSR) synthetic aperture radar (SAR)

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