Three-dimensional space-borne synthetic aperture radar (SAR) imaging with multiple pass processing

Three-dimensional (3D) synthetic aperture radar (SAR) imaging via multiple-pass processing is an extension of interferometric SAR imaging. It exploits more than two flight passes to achieve a desired resolution in elevation. In this paper, a novel approach is developed to reconstruct a 3D space-borne SAR image with multiple-pass processing. It involves image registration, phase correction and elevational imaging. An image model matching is developed for multiple image registration, an eigenvector method is proposed for the phase correction and the elevational imaging is conducted using a Fourier transform or a super-resolution method for enhancement of elevational resolution. 3D SAR images are obtained by processing simulated data and real data from the first European Remote Sensing satellite (ERS-1) with the proposed approaches.     

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization基于伪极坐标变换和原子范数最小化的网格偏离目标DLSLA 3-D SAR成像方法

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.     

Kinect based real-time synthetic aperture imaging through occlusion

Real-time and high performance occluded object imaging is a big challenge to many computer vision applications. In recent years, camera array synthetic aperture theory proves to be a potential powerful way to solve this problem. However, due to the high cost of complex system hardware, the severe blur of occluded object imaging, and the slow speed of image processing, the exiting camera array synthetic aperture imaging algorithms and systems are difficult to apply in practice. In this paper, we present a novel handheld system to handle those challenges. The objective of this work is to design a convenient system for real-time high quality object imaging even under severe occlusion. The main characteristics of our work include: (1) To the best of our knowledge, this is the first real-time handheld system for seeing occluded object in synthetic imaging domain using color and depth images. (2) A novel sequential synthetic aperture imaging framework is designed to achieve seamless interaction among multiple novel modules, and this framework includes object probability generation, virtual camera array generation, and sequential synthetic aperture imaging. (3) In the virtual camera array generation module, based on the integration of color and depth information, a novel feature set iterative optimization algorithm is presented, which can improve the robustness and accuracy of camera pose estimation even in dynamic occlusion scene. Experimental results in challenging scenarios demonstrate the superiority of our system both in robustness and efficiency compared against the state-of-the-art algorithms.     

An improvement method for circular synthetic aperture radar imaging

Circular synthetic aperture radar (CSAR) is the imaging mode when the radar moves along a circular path and the observed area is always covered by the wave beam. It is different from traditional SAR modes (strip-map SAR and spotlight SAR) and has potential advantages such as 360° observation, target recognition, and three-dimensional reconstruction. According to the imaging processing of CSAR, motion error is an important issue affecting the CSAR image quality, but the motion compensatio n (MOCO) method for CSAR is underdeveloped. Accordingly, with detailed analysis the motion error model is established and a data-driven MOCO flow chart for CSAR is proposed. The real CSAR data are used to verify the proposed method.     

Review Article. The imaging of waves by satelliteborne synthetic aperture radar: the effects of sea-surface motion

The effects of sea-surface velocities in the imaging of waves by synthetic aperture radar (SAR) are considered using the ‘facet’ concept of the backscattering process. It is shown that if the sea wave spectrum is divided at the nominal limit of resolution of the SAR the effect of the long and short wavelength parts can be considered separately, the former being treated by numerical simulation and the latter by statistical methods. It is found that the motions due to: the short wavelengths produce an azimuthal smearing which can be represented by a Gaussian low-pass filter acting on the azimuthal component of wavenumber in the image. The cut-off wavelength is typically some hundreds of metres in moderate winds. Images obtained with the SEASAT SAR frequently show such an effect.     

基于深度协同稀疏编码网络的海洋浮筏SAR图像目标识别

浮筏养殖广泛存在于我国近海海域, 可见光遥感图像无法完全准确地获取养殖目标, 而基于主动成像的合成孔径雷达(Synthetic aperture radar, SAR)遥感图像能够得到养殖目标, 因此采用SAR图像进行海洋浮筏养殖目标识别. 然而, 海洋遥感SAR图像包含大量相干斑噪声, 并且SAR图像特征单一, 使得目标识别难度较大. 为解决这些问题, 提出一种深度协同稀疏编码网络(Deep collaborative sparse coding network, DCSCN)进行海洋浮筏识别. 本文方法对预处理后的图像先提取纹理特征和轮廓特征, 再进行超像素分割并将同一个超像素块特征组输入该网络进行协同表示, 最后得到有效特征并分类识别. 通过人工SAR图像和北戴河海域浮筏养殖SAR图像的实验验证所提模型的有效性. 该网络不仅具有优异的特征表示能力, 能够获得更适合分类器的特征, 而且通过近邻协同约束, 有效抑制相干斑噪声影响, 所以提高了SAR图像目标识别精度.     

Monte-Carlo simulation studies of the nonlinear imaging of a two dimensional surface wave field by a synthetic aperture radar

Abstract

The imaging of ocean surface waves by synthetic aperture radar (SAR) is investigated using two-dimensional Monte-Carlo simulations. The properties of the SAR imaging mechanism for windseas and swell in the Bragg scattering regime are discussed as a function of a few governing non-dimensional parameters formed from a combination of SAR and ocean wave parameters. The parameter ranges may be classified into three regimes corresponding to linear and weakly nonlinear, medium nonlinear and strongly nonlinear imaging. The nonlinearities are induced by motion effects (velocity bunching, velocity spread and acceleration smearing), while the real aperture radar (RAR) tilt and hydrodynamic modulation processes are regarded as linear. In the strongly nonlinear imaging regime, the velocity bunching mechanism causes a rotation of the spectral peak towards the range direction and a stretching of the peak wavelength. In addition, the azimuthal resolution is degraded through the Doppler spreading arising from the different facet velocities within a SAR resolution cell. The imaging properties in this regime are largely governed by two non-dimensional parameters, the velocity bunching and velocity smearing parameter. The nonlinear imaging distortions are strongest for broad spectra (windseas) and are significantly weaker for narrow-band swell. In the linear and weakly nonlinear imaging regime, the superposition of the hydrodynamic and tilt cross-section modulation and the velocity bunching transfer function normally produces a rotation of the spectral peak towards the azimuthal direction. The interference characteristics of these different modulation mechanisms depends on the wave propagation direction and can lead to a significant distortion of the image. This is often seen in large differences in the image modulation depths of waves propagating parallel and anti-parallel to the flight direction.     

基于压缩感知的遥感成像稀疏重构性能分析

压缩感知是一种新型的信息论，打破了传统的Shannon-Nyquist采样定理，能够以少量数据完成信号采样。稀疏重构是压缩感知由理论到实际的关键环节，为了将压缩感知有效地应用于遥感成像领域，研究了稀疏重构对遥感成像过程的影响。针对稀疏重构理论模型，分析了重构误差的成因；同时，针对典型的凸优化类算法和贪婪类算法，利用峰值信噪比指标对遥感图像重构误差进行评价。在仿真实验中，定量考察遥感图像在不同压缩采样率、不同重构算法下的稀疏重构性能。结果表明，稀疏重构算法能够成功重构遥感图像，各算法在不同压缩采样率下均表现出了较好的重构质量，整体上能够满足遥感成像应用，验证了压缩感知稀疏重构方法在遥感成像中应用的可行性。     

Method and architecture to accelerate multi-element synthetic aperture imaging

Multi-element synthetic aperture focusing (n-SAF) methods have been proposed as a suitable way to reduce cost and size for complex ultrasonic imaging systems. In this method, the larger the sub-aperture, the better the image contrast. However, when the number n of elements in the sub-aperture increases, some problems arise due to the great number of signals involved in the beamforming process, demanding a more complex electronic architecture, a higher bandwidth to manage the signals, and a greater computational power to compose the images in real time.This paper presents the n-SAF method in combination with a data reduction algorithm that reduces the number of signals intervening in the image data processing. The proposed method, called nR-SAF, simplifies the beamforming process; the hardware adds all signals in phase from identical coarray elements (similar spatial frequencies) thus a reduction of approximately 2/n of the data intervening in the dynamical focusing process is attained.The phase errors due to the simplified algorithms are also analyzed. We concluded that there are some limitations for sub-aperture size when image points are very near the transducer. Finally, an electronic architecture is presented, which is able to implement high velocity images from nR-SAF methods.     

下视SAR数据3维表面重建

目的 合成孔径雷达(SAR)因成像方法、几何角度等原因使得采集到的数据具有稀疏性及残缺性,如果直接用其进行建模,不能真实地还原物体。针对下视SAR数据的特点,提出一种在建模过程中能够自动修补稀疏及残缺数据的重建方法。方法 首先引入大津法对3维SAR数据进行预处理,然后将2维图像分割方法中的Chan-Vese模型推广应用到下视SAR数据的表面重建中,在初始表面及轮廓指示函数的求取过程中引入距离函数和内积函数。结果 将本文方法与等值面抽取法的重建结果进行比较,本文方法在重建的过程中能够自动修补空洞,重建出的模型表面更加光滑,能更加真实地反映原物体的特征。结论 可以将本文方法推广应用到稀疏及残缺SAR数据的建模中。     

基于互补信息特征的SAR与可见光图像融合研究

由于成像方式及波谱接收段的不同，合成孔径雷达SAR（synthetic aperture radar）与可见光图像所反映的信息有很大差异，图像之间相关性弱，且互补性明显。因此在图像融合时，应该根据其互补性信息特征各取所长。在研究了一些现有融合方法的基础上，提出了一种基于互补信息特征的SAR与可见光图像融合方法。首先通过基于像素邻域的能量统计特性融合SAR与可见光图像，将SAR图像中的重要目标信息加入到可见光图像中，然后再利用小波变换进行二次融合，充分加入原始图像的边缘细节信息。实验结果表明，该融合方法有效。     

SAR image segmentation based on quantum-inspired multiobjective evolutionary clustering algorithm

The segmentation task in the feature space of an image can be formulated as an optimization problem. Recent researches have demonstrated that the clustering techniques, using only one objective may not obtain suitable solution because the single objective function just can provide satisfactory result to one kind of corresponding data set. In this letter, a novel multiobjective clustering approach, named a quantum-inspired multiobjective evolutionary clustering algorithm (QMEC), is proposed to deal with the problem of image segmentation, where two objectives are simultaneously optimized. Based on the concepts and principles of quantum computing, the multi-state quantum bits are used to represent individuals and quantum rotation gate strategy is used to update the probabilistic individuals. The proposed algorithm can take advantage of the multiobjective optimization mechanism and the superposition of quantum states, and therefore it has a good population diversity and search capabilities. Due to a set of nondominated solutions in multiobjective clustering problems, a simple heuristic method is adopted to select a preferred solution from the final Pareto front and the results show that a good image segmentation result is selected. Experiments on one simulated synthetic aperture radar (SAR) image and two real SAR images have shown the superiority of the QMEC over three other known algorithms.     

Range ambiguity suppression in a synthetic aperture radar using pulse phase coding and two-pulse cancellation

ABSTRACT

In the presence of range ambiguity, the synthetic aperture radar (SAR) systems suffer from image aliasing, which dramatically degrades the quality of SAR images. In this article, an easy-to-implement technique for range ambiguity suppression is investigated, which is based on phase coding in the transmit pulse dimension, referred to as pulse phase coding (PPC). By properly designing the PPC series, it is possible to discriminate the range ambiguous echoes from different range areas in the Doppler frequency domain. To further suppress the range ambiguous echo, a two-pulse cancellation (TPC)-based SAR imaging method is proposed, which improves the quality of SAR images in the presence of range ambiguity. The proposed two-pulse cancellation is performed followed by the azimuth compression. The simulation results demonstrate the effectiveness of the proposed method.     

Fast implementation of ground maneuvering target focusing

Ground maneuvering target detection is a hot topic in the applications of synthetic aperture radar (SAR), whereas its focusing performance is severely deteriorated by range migration and Doppler frequency migration during a long integration time. This paper proposes a novel method to image the target and estimate its parameters via performing two independent 2-dimensional (2-D) searches after a parameter separation operation. In order to improve the search speed, we set the limited search ranges and propose local mapping sparse Fourier transform (LMSFT) to replace fast Fourier transform (FFT). Compared with the traditional algorithms, the proposed method can realize fast coherent integration of multiple maneuvering targets via compensating the high-order range migration and Doppler frequency migration. In addition, the proposed method is stable under noise. Several simulation results have validated the effectiveness of the proposed method.     

A novel multi-object detection method in complex scene using synthetic aperture imaging

This paper proposes a novel multi-object detection method using multiple cameras. Unlike conventional multi-camera object detection methods, our method detects multiple objects using a linear camera array. The array can stream different views of the environment and can be easily reconfigured for a scene compared with the overhead surround configuration. Using the proposed method, the synthesized results can provide not only views of significantly occluded objects but also the ability of focusing on the target while blurring objects that are not of interest. Our method does not need to reconstruct the 3D structure of the scene, can accommodate dynamic background, is able to detect objects at any depth using a new synthetic aperture imaging method based on a simple shift transformation, and can see through occluders. The experimental results show that the proposed method has a good performance and can synthesize objects located within any designated depth interval with much better clarity than that using an existing method. To our best knowledge, it is the first time that such a method using synthetic aperture imaging has been proposed and developed for multi-object detection in a complex scene with a significant occlusion at different depths.     

地球同步轨道SAR快速BP成像算法

地球同步轨道合成孔径雷达（Geosynchronous Synthetic Aperture Radar，GEO SAR）轨道高，合成孔径长，采用局部近似处理的后向投影算法（Local Back Projection，LBP）不再适用。结合传统LBP算法原理，构建了基于LBP的GEO SAR相位误差模型，提出了适用于GEO SAR的LBP成像处理方法。仿真结果表明改进后的LBP算法在保证成像质量与传统BP算法基本相近的同时，将计算效率提升了75倍。     

基于压缩感知的SAR图像鲁棒编码传输

目的 尽管传统的联合信源信道编码方案可以获得高效的压缩性能,但当信道恶化超过信道编码的纠错能力时会导致解码端重构性能的急剧下降;为此利用压缩感知的民主性提出一种鲁棒的SAR图像编码传输方案,且采用了一系列方法提高该方案的率失真性能。方法 考虑到SAR图像丰富的边缘信息,采用具有更强方向表示能力的方向提升小波变换(DLWT)对SAR图像进行稀疏表示,且为消除压缩感知中恢复非稀疏信号时存在的混叠效应,采用了稀疏滤波方法保证大系数的精确恢复,在解码端采用了高效的Bayesian重建算法获得图像的高性能重建。结果 在同等码率下,与传统的联合信源信道编码方案CCSDS-RS相比,本文方案可以实现更加鲁棒的编码传输,当丢包率达到0.05时,本文方案DSFB-CS获得的重建性能明显要高于CCSDS-RS;与基于Bayesian重建算法TSW-CS的传统方案相比,本文方案可提高峰值信噪比(PSNR)3.9 dB。结论 本文方案DSFB-CS 实现了SAR图像的鲁棒传输,随着丢包率的上升,DSFB-CS获得的重建性能缓慢下降,保证了面对不稳定信道时,解码端可以获得相对稳定的重构图像。     

稀疏阵列的波束形成与控制方法

首先从信号与信息处理的角度阐述了波束形成所要解决的技术问题和波束形成的理论优势和方法局限,并对传统的基于空域波形采样的波束形成技术进行了再思考。其次,分析了稀疏阵列的布阵特点对波束形成技术的挑战,给出了空域、时域、频域分布式相参信号处理等关键技术及其理论性能的分析与比较结果,并利用实例分析了空、时、频协同采样克服低维欠采样模糊的可行性。最后分析了在实际应用中遇到的一些非理想因素对稀疏阵列波束形成与控制的影响及其解决方法。     

A statistical distribution texton feature for synthetic aperture radar image classification

We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.     

Label-dependent sparse representation for synthetic aperture radar target configuration recognition

Synthetic aperture radar (SAR) target images suffer from target aspect angle sensitivity. To overcome the obstacle that seriously influences recognition performance, a label-dependent sparse representation (LSR) algorithm is proposed to realize SAR target configuration recognition in the sparse domain. The label of the training sample is embedded into the sparse representation (SR) model, and dictionaries are constructed individually to eliminate disturbances. LSR is implemented according to a statistical model based on the Gaussian mixture distribution (GMD). Experiments are conducted on a wide range of moving and stationary target acquisition and recognition (MSTAR) databases. The experimental results demonstrate the effectiveness of the proposed algorithm, which outperforms other existing algorithms in terms of recognition accuracy.