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Xiaoling Zhang

Publications and source records attributed to Xiaoling Zhang.

At least 37 records · Page 2Linked to original sources
arXiv2022

Near-filed SAR Image Restoration with Deep Learning Inverse Technique: A Preliminary Study

Benefiting from a relatively larger aperture's angle, and in combination with a wide transmitting bandwidth, near-field synthetic aperture radar (SAR) provides a high-resolution image of a target's scattering distribution-hot spots. Meanwhile, imaging result suffers inevitable degradation from sidelobes, clutters, and noises, hindering the information retrieval of the target. To restore the image, current methods make simplified assumptions; for example, the point spread function (PSF) is spatially consistent, the target consists of sparse point scatters, etc. Thus, they achieve limited restoration performance in terms of the target's shape, especially for complex targets. To address these issues, a preliminary study is conducted on restoration with the recent promising deep learning inverse technique in this work. We reformulate the degradation model into a spatially variable complex-convolution model, where the near-field SAR's system response is considered. Adhering to it, a model-based deep learning network is designed to restore the image. A simulated degraded image dataset from multiple complex target models is constructed to validate the network. All the images are formulated using the electromagnetic simulation tool. Experiments on the dataset reveal their effectiveness. Compared with current methods, superior performance is achieved regarding the target's shape and energy estimation.

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arXiv2022

A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging

Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimensional deep unfolding imaging network is constructed. On the basis of it, we add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Meanwhile, to train the proposed multifeature-based imaging network, we construct a tomoSAR simulation dataset consisting entirely of simulation data of buildings. Experiments verify the effectiveness of the model. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.

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arXiv2022

Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network

With the booming of Convolutional Neural Networks (CNNs), CNNs such as VGG-16 and ResNet-50 widely serve as backbone in SAR ship detection. However, CNN based backbone is hard to model long-range dependencies, and causes the lack of enough high-quality semantic information in feature maps of shallow layers, which leads to poor detection performance in complicated background and small-sized ships cases. To address these problems, we propose a SAR ship detection method based on Swin Transformer and Feature Enhancement Feature Pyramid Network (FEFPN). Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps. FEFPN is proposed to further improve the quality of feature maps by gradually enhancing the semantic information of feature maps at all levels, especially feature maps in shallow layers. Experiments conducted on SAR ship detection dataset (SSDD) reveal the advantage of our proposed methods.

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arXiv2022

3D Super-Resolution Imaging Method for Distributed Millimeter-wave Automotive Radar System

Millimeter-wave (mmW) radar is widely applied to advanced autopilot assistance systems. However, its small antenna aperture causes a low imaging resolution. In this paper, a new distributed mmW radar system is designed to solve this problem. It forms a large sparse virtual planar array to enlarge the aperture, using multiple-input and multiple-output (MIMO) processing. However, in this system, traditional imaging methods cannot apply to the sparse array. Therefore, we also propose a 3D super-resolution imaging method specifically for this system in this paper. The proposed method consists of three steps: (1) using range FFT to get range imaging, (2) using 2D adaptive diagonal loading iterative adaptive approach (ADL-IAA) to acquire 2D super-resolution imaging, which can satisfy this sparsity under single-measurement, (3) using constant false alarm (CFAR) processing to gain final 3D super-resolution imaging. The simulation results show the proposed method can significantly improve imaging resolution under the sparse array and single-measurement.

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arXiv2022

Two Dimensional Sparse-Regularization-Based InSAR Imaging with Back-Projection Embedding

Interferometric Synthetic Aperture Radar (InSAR) Imaging methods are usually based on algorithms of match-filtering type, without considering the scene's characteristic, which causes limited imaging quality. Besides, post-processing steps are inevitable, like image registration, flat-earth phase removing and phase noise filtering. To solve these problems, we propose a new InSAR imaging method. First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded. Second, to avoid the post processing steps, we establish a new forward observation process, where the back-projection imaging method is embedded. Third, a forward and backward iterative solution method is proposed based on proximal gradient descent algorithm. Experiments on simulated and measured data reveal the effectiveness of the proposed method. Compared with the conventional method, higher quality interferogram can be obtained directly from raw echoes without post-processing. Besides, in the under-sampling situation, it's also applicable.

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arXiv2022

Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection

The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR. The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background of ViSAR for moving target detection. In this work, the proposed method is used to suppress background; then, we use several target detection networks to detect the moving target shadows. The experimental result shows that the proposed method can effectively suppress the interference of complicated back-ground information and improve the accuracy of moving target shadow detection in ViSAR.

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arXiv2022

AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features

Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on deep learning can effectively reduce computational costs. The idea of existing researches is to reconstruct the elevation for each range-azimuth cell in one-dimensional using a deep-unfolding network. However, since these methods are commonly sensitive to signal sparsity level, it usually leads to some drawbacks like continuous surface fractures, too many outliers, \textit{et al}. To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed. By adding a U-Net-like structure, AETomo-Net performs reconstruction by each azimuth-elevation slice and adds 2D features extraction and fusion capabilities to the original deep unrolling network. In this way, each azimuth-elevation slice can be reconstructed with richer features and the quality of the imaging results will be improved. Experiments show that the proposed method can effectively solve the above defects while ensuring imaging accuracy and computation speed compared with the traditional ISTA-based method and CV-LISTA.

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arXiv2022

Near-Field SAR Image Restoration Based On Two Dimensional Spatial-Variant Deconvolution

Images of near-field SAR contains spatial-variant sidelobes and clutter, subduing the image quality. Current image restoration methods are only suitable for small observation angle, due to their assumption of 2D spatial-invariant degradation operation. This limits its potential for large-scale objects imaging, like the aircraft. To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed. First, the image degradation is seen as a complex convolution process with 2D spatial-variant operations. Then, to restore the image, the process of deconvolution is performed by cyclic coordinate descent algorithm. Experiments on simulation and measured data validate the effectiveness and superiority of the proposed method. Compared with current methods, higher precision estimation of the targets' amplitude and position is obtained.

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arXiv2022

Constant-Time-Delay Interferences In Near-Field SAR: Analysis And Suppression In Image Domain

Inevitable interferences exist for the SAR system, adversely affecting the imaging quality. However, current analysis and suppression methods mainly focus on the far-field situation. Due to different sources and characteristics of interferences, they are not applicable in the near field. To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work. We find that echoes from both the nadir points and the antenna coupling are the main causes, which have the constant-time-delay feature. To characterize this, we further establish an analytical model. It reveals that their patterns in 1D, 2D and 3D imaging results are all comb-like, while those of targets are point-like. Utilizing these features, a suppression method in image domain is proposed based on low-rank reconstruction. Measured data are used to validate the correctness of our analysis and the effectiveness of the suppression method.

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arXiv2022

A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation

Most of existing synthetic aperture radar (SAR) ship in-stance segmentation models do not achieve mask interac-tion or offer limited interaction performance. Besides, their multi-scale ship instance segmentation performance is moderate especially for small ships. To solve these problems, we propose a mask attention interaction and scale enhancement network (MAI-SE-Net) for SAR ship instance segmentation. MAI uses an atrous spatial pyra-mid pooling (ASPP) to gain multi-resolution feature re-sponses, a non-local block (NLB) to model long-range spa-tial dependencies, and a concatenation shuffle attention block (CSAB) to improve interaction benefits. SE uses a content-aware reassembly of features block (CARAFEB) to generate an extra pyramid bottom-level to boost small ship performance, a feature balance operation (FBO) to improve scale feature description, and a global context block (GCB) to refine features. Experimental results on two public SSDD and HRSID datasets reveal that MAI-SE-Net outperforms the other nine competitive models, better than the suboptimal model by 4.7% detec-tion AP and 3.4% segmentation AP on SSDD and by 3.0% detection AP and 2.4% segmentation AP on HRSID.

cs.CV↗
arXiv2020

NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new version of the previous NTIRE 2019 challenge on real image denoising that was based on the SIDD benchmark. This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+. This challenge has two tracks for quantitatively evaluating image denoising performance in (1) the Bayer-pattern rawRGB and (2) the standard RGB (sRGB) color spaces. Each track ~250 registered participants. A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge. The proposed methods by the participating teams represent the current state-of-the-art performance in image denoising targeting real noisy images. The newly collected SIDD+ datasets are publicly available at: https://bit.ly/siddplus_data.

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arXiv2019

Background modelling for $γ$-ray spectroscopy with INTEGRAL/SPI

The coded-mask spectrometer-telescope SPI on board the INTEGRAL observatory records photons in the energy range between 20 and 8000 keV. A robust and versatile method to model the dominating instrumental background (BG) radiation is difficult to establish for such a telescope in the rapidly changing space environment. From long-term monitoring of SPI's Germanium detectors, we built up a spectral parameter data base, which characterises the instrument response as well as the BG behaviour. We aim to build a self-consistent and broadly applicable BG model for typical science cases of INTEGRAL/SPI, based on this data base. The general analysis method for SPI relies on distinguishing between illumination patterns on the 19-element Germanium detector array from BG and sky in a maximum likelihood framework. We illustrate how the complete set of measurements, even including the exposures of the sources of interest, can be used to define a BG model. We apply our method to different science cases, including point-like, diffuse, continuum, and line emission, and evaluate the adequacy in each case. From likelihood values and the number of fitted parameters, we determine how strong the impact of the unknown BG variability is. We find that the number of fitted parameters, i.e. how often the BG has to be re-normalised, depends on the emission type (diffuse with many observations over a large sky region, or point-like with concentrated exposure around one source), and the spectral energy range and bandwidth. A unique time scale, valid for all analysis issues, is not applicable for INTEGRAL/SPI, but must and can be inferred from the chosen data set. We conclude that our BG modelling method is usable in a large variety of INTEGRAL/SPI science cases, and provides nearly systematics-free and robust results.

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arXiv2017

INTEGRAL/SPI $γ$-ray line spectroscopy

INTEGRAL/SPI is a space-based coded mask telescope featuring a 19-element Germanium detector array for high-resolution gamma-ray spectroscopy, encapsulated in a scintillation detector assembly that provide a veto for background from charged particles. In space, cosmic rays irradiate spacecraft and instrument, which results in a large instrumental background from activation of those materials, and leads to deterioration of the charge collection properties of the Ge detectors. We use 13.5 years of INTEGRAL/SPI spectra for each detector and for each pointing of the satellite. Spectral fits provide us with details about separated background components. From the strongest background lines, we first determine how the spectral response changes with time. We then determine how the instrumental background components change in intensities and other characteristics, most-importantly their relative distribution among detectors. Spectral resolution of Ge detectors in space degrades with time, up to 15% within half a year, consistently for all detectors, and across the SPI energy range. Semi-annual annealing operations recover these losses, yet there is a small long-term degradation. The intensity of instrumental background varies anti-correlated to solar activity, in general. There are significant differences among different lines and with respect to continuum. Background lines are found to have a characteristic, well-defined and long-term consistent intensity ratio among detectors. We use this to categorise lines in groups of similar behaviour. The dataset of spectral-response and background parameters as fitted across the INTEGRAL mission allows studies of SPI spectral response and background behaviour in a broad perspective, and efficiently supports precision modelling of instrumental background.

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arXiv2016

Positron annihilation signatures associated with the outburst of the microquasar V404 Cygni

Microquasars are stellar-mass black holes accreting matter from a companion star and ejecting plasma jets at almost the speed of light. They are analogues of quasars that contain supermassive black holes of $10^6$ to $10^{10}$ solar masses. Accretion in microquasars varies on much shorter timescales than in quasars and occasionally produces exceptionally bright X-ray flares. How the flares are produced is unclear, as is the mechanism for launching the relativistic jets and their composition. An emission line near 511 kiloelectronvolts has long been sought in the emission spectrum of microquasars as evidence for the expected electron-positron plasma. Transient high-energy spectral features have been reported in two objects, but their positron interpretation remains contentious. Here we report observations of $γ$-ray emission from the microquasar V404 Cygni during a recent period of strong flaring activity. The emission spectrum around 511 kiloelectronvolts shows clear signatures of variable positron annihilation, which implies a high rate of positron production. This supports the earlier conjecture that microquasars may be the main sources of the electron-positron plasma responsible for the bright diffuse emission of annihilation $γ$-rays in the bulge region of our Galaxy. Additionally, microquasars could be the origin of the observed megaelectronvolt continuum excess in the inner Galaxy.

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arXiv2015

Gamma-ray spectroscopy of Positron Annihilation in the Milky Way

The annihilation of positrons in the Galaxy's interstellar medium produces characteristic gamma-rays with a line at 511 keV. This emission has been observed with the spectrometer SPI on INTEGRAL, confirming a puzzling morphology with bright emission from an extended bulge-like region, and faint disk emission. Most plausible sources of positrons are believed to be distributed throughout the disk of the Galaxy. We aim to constrain characteristic spectral shapes for different spatial components in the disk and bulge with the high-resolution gamma-ray spectrometer SPI, based on a new instrumental background method and detailed multi-component sky model fitting. We confirm the detection of the main extended components of characteristic annihilation gamma-ray signatures at 58$σ$ significance in the line. The total Galactic line intensity amounts to $(2.7\pm0.3)\times10^{-3}~\mathrm{ph~cm^{-2}~s^{-1}}$ for our assumed spatial model. We derive spectra for the bulge and disk, and a central point-like and at the position of Sgr A*, and discuss spectral differences. The bulge shows a 511 keV line intensity of $(0.96\pm0.07)\times10^{-3}~\mathrm{ph~cm^{-2}~s^{-1}}$ and an ortho-positronium continuum equivalent to a positronium fraction of ($1.08\pm0.03$). The 2D-Gaussian representing the disk has an extent of $60^{+10}_{-5}$ deg in longitude and a latitudinal extent of $10.5^{+2.5}_{-1.5}$ deg; the line intensity is $(1.7\pm0.4)\times10^{-3}~\mathrm{ph~cm^{-2}~s^{-1}}$ with a marginal detection of ortho-positronium and an overall diffuse Galactic continuum of $(5.85\pm1.05)\times10^{-5}~\mathrm{ph~cm^{-2}~s^{-1}~keV^{-1}}$ at 511 keV. The disk shows no flux asymmetry between positive and negative longitudes, although spectral details differ. The flux ratio between bulge and disk is ($0.6\pm0.1$). The central source has an intensity of $(0.8\pm0.2)\times10^{-4}~\mathrm{ph~cm^{-2}~s^{-1}}$.

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arXiv2014

SN2014J gamma-rays from the 56Ni decay chain

The measurement of gamma-ray lines from the decay chain of 56Ni provides unique information about the explosion in supernovae. The 56Ni freshly-produced in the supernova powers the optical light curve, as it emits gamma-rays upon its radioactive decay to 56Co and then 56Fe. Gamma-ray lines from 56Co decay are expected to become directly visible through the overlying white dwarf material several weeks after the explosion, as they progressively penetrate the overlying material of the supernova envelope, diluted as it expands. The lines are expected to be Doppler-shifted or broadened from the kinematics of the 56Ni ejecta. With the SPI spectrometer on INTEGRAL and using an improved instrumental background method, we detect the two main lines from 56Co decay at 847 and 1238 keV from SN2014J at 3.3 Mpc, significantly Doppler-broadened, and at intensities (3.65+/-1.21) 10^-4 and (2.27+/-0.69) 10^-4 ph cm^-2s^-1, respectively, at brightness maximum. We measure their rise towards a maximum after about 60-100 days and decline thereafter. The intensity ratio of the two lines is found consistent with expectations from 56Co decay (0.62+/-0.28 at brightness maximum, expected is 0.68). We find that the broad lines seen in the late, gamma-ray transparent phase are not representative for the early gamma-ray emission, and rather notice the emission spectrum to be complex and irregular until the supernova is fully transparent to gamma-rays, with progressive uncovering of the bulk of 56Ni. We infer that the explosion morphology is not spherically symmetric, both in the distribution of 56Ni and of the unburnt material which occults the 56Co emission. Comparing light curves from different plausible models, the resulting 56Ni mass is determined as 0.49+/-0.09 M_sol.

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