Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.

Design of synthetic aperture radar low-intercept radio frequency stealth

Not confined to a certain point,such as waveform,this paper systematically studies the low-intercept radio frequency(RF)stealth design of synthetic aperture radar(SAR)from the system level.The study is carried out from two levels.In the first level,the maximum low-intercept range equation of the conventional SAR system is deduced firstly,and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced.In the second level,the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation.Finally,the main technical characteristics of the lowintercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.

IMAGING AND MTI PROCESSING BASED ON DUAL-FREQUENCIES DUAL-APERTURES SPACEBORNE SAR

Based on dual-frequencies dual-apertures spaceborne SAR （Synthetic Aperture Radar）, a new SAR system with four receiving channels and two operation modes is presented in this paper, SAR imaging and Moving Target Indication （MTI） are studied in this system. High resolution imaging with wide swath is implemented by the Mode Ⅰ, and MTI is completed by the Mode Ⅱ. High azimuth resolution is achieved by the Displaced Phase Center （DPC） multibeam technique. And the Coherent Accumulation （CA） method, which combines dual channels data of different carrier frequency, is used to enhance the range resolution. For the data of different carrier frequency, the two aperture interferometric processing is executed to implement clutter cancellation, respectively. And the couple of clutter suppressed data are employed to implement Dual Carrier Frequency Conjugate Processing （DCFCP）, then both slow and fast moving targets detection can be completed, followed by moving target imaging. The simulation results show the validity of the signal processing method of this new SAR system.

CROSS-TRACK THREE APERTURES MILLIMETER WAVE SAR SIDE-LOOKING THREE-DIMENSIONAL IMAGING

The airborne cross-track three apertures MilliMeter Wave (MMW) Synthetic Aperture Radar (SAR) side-looking three-Dimensional (3D) imaging is investigated in this paper. Three apertures are distributed along the cross-track direction, and three virtual phase centers will be obtained through one-input and three-output. These three virtual phase centers form a sparse array which can be used to obtain the cross-track resolution. Because the cross-track array is short, the cross-track resolution is low. When the system works in side-looking mode, the cross-track resolution and height resolution will be coupling, and the low cross-track resolution will partly be transformed into the height uncertainty. The beam pattern of the real aperture is used as a weight to improve the Peak to SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the cross-track sparse array. In order to suppress the high cross-track sidelobes, a weighting preprocessing method is proposed. The 3D images of a point target and a simulation scene are achieved to verify the feasibility of the proposed method. And the imaging result of the real data obtained by the cross-track three-baseline MMW InSAR prototype is presented as a beneficial attempt.

SAR IMAGE RECOGNITION BASED ON MULTI-ASPECT OF SHADOW INFORMATION

The traditional synthetic aperture radar（SAR） image recognition techniques focus on the electro magnetic （EM） scattering centers, ignoring the important role of the shadow information on the SAR image recognition. It is difficult to classify targets by the shadow information independently, because the shadow shape is dependent on the radar aspect angle, the depression angle and the resolution. Moreover, the shadow shapes of different targets are similar. When the multiple SAR images of one target from different aspects are available, the performance of the target recognition can be improved. Aimed at the problem, a multi-aspect SAR image recognition technique based on the shadow information is developed. It extracts shadow profiles from SAR images, and takes chain codes as the feature vectors of targets. Then, feature vectors on multiple aspects of the same target are combined with feature sequences, and the hidden Markov model （HMM） is applied to the feature sequences for the target recognition. The simulation result shows the effectiveness of the method.

微波视觉与SAR图像智能解译

高分辨率雷达成像技术和人工智能、大数据技术的快速发展,有力促进了雷达图像智能解译技术的进步。由于雷达传感器本身的特殊性和电磁散射成像物理的复杂性,雷达图像的解译缺乏光学图像的直观性,准确迅速识别分类的需求对雷达图像解译提出了迫切的挑战。在借鉴人脑光视觉感知机理和计算机视觉图像处理相关技术基础上,进一步融合电磁散射物理规律及其雷达成像机理,我们提出发展微波域雷达图像解译的“微波视觉”的新交叉领域研究。该文介绍微波视觉的概念与内涵,提出微波视觉认知模型,阐述其基础理论问题与技术路线,最后介绍了作者团队在相关问题上的初步研究进展。

Suppression of Speckle in SAR Images Using Wavelet-Based HMM

In order to suppress the speckle appearing in synthesis aperture radar (SAR) images, a novel speckle reduction method based on wavelet domain hidden Markov tree (HMT) was proposed. First, the image was logarithmic transformed to change the statistical property of the speckles. Then an HMT was constructed in the correspondent wavelet domain. Based on this model, the image signal was restored by maximum likelihood estimation and speckle noise was suppressed. Simulating SAR images had shown that the performance of the filter is satisfactory for both speckle smoothing and edges presentation, and for generating visually natural images as well.

多域特征引导的无监督SAR图像舰船检测方法

如何在合成孔径雷达(SAR)图像标注样本有限的条件下,提升舰船检测性能一直是SAR图像处理中的热点问题。本文提出一种多域特征引导的无监督域适应方法,将知识从有标注的源域(光学图像)转移到未标注的目标域(SAR图像),降低对标记SAR图像数据依赖。同时,设计了频域转换模块、注意力区域增强模块和自适应权重模块来缩小光学、SAR图像域之间的域差距,提高源域与目标域特征对齐效率,增强网络在挑战性样本下的特征迁移能力。在公开发布的数据集上进行了大量实验。结果表明:所提的模块较基础模型AP50提升10%,总体性能优于其他先进的方法。

考虑速度聚束效应的SAR海浪成像仿真方法

针对合成孔径雷达(SAR)海浪成像仿真技术对于速度聚束效应的考虑不充分,仿真数据无法准确反映实际SAR数据的问题,建立一种充分考虑速度聚束效应的SAR海浪成像仿真方法并进行了实验验证。计算了SAR图像强度的概率密度分布的仿真结果与理论结果之间的均方误差(MSE),在风速分别为5、10和15 m/s时,考虑速度聚束效应前、后比对结果的MSE分别为0.1012、0.1576、0.0556与0.0179、0.0314、0.0088。仿真结果表明,所提SAR海浪成像仿真方法有效提高了仿真数据的准确性,对SAR海浪成像的应用具有一定实用价值。

基于向量法近似的圆弧SAR成像方法

Arc SAR是一种能够在短时间内可重复扫描360°的雷达成像技术,具有重访周期短,观测范围广等优点,在边坡检测、山体滑坡等场景中有着广泛的应用前景。在Arc SAR模式下雷达随着机械臂进行圆周运动来形成合成孔径,因其运动轨迹不是直线,所以不能直接运用传统直线导轨的成像方法,需为Arc SAR设计新的成像算法,然而现有的成像方法不能很好地平衡精度和复杂度。在距离多普勒算法中,由于采用泰勒展开来近似斜距,导致成像质量不佳,或是需采取分割策略进行成像。因此,提出了一种新的距离多普勒算法,利用目标与雷达的向量关系进行近似,求得了近似斜距表达式,继而得到了距离多普勒域的信号表达式。基于这一表达式,提出了相应的相位误差补偿方法,完成了图像聚焦。最后通过点目标仿真验证了该算法的有效性。

基于多层显著性模型的SAR图像舰船目标检测

针对合成孔径雷达图像舰船目标检测问题,提出了一种结合选择机制与轮廓信息的多层显著性目标检测方法。首先,利用非下采样剪切波和频谱残差法进行全局显著性区域提取。其次,提出了一种基于动态恒虚警率的活动轮廓显著性模型,逐步滤除候选区域的虚警,提取目标轮廓,从而实现目标的精确检测。所提方法能够由粗到细地快速捕获目标区域,从而实现高效、高分辨率合成孔径雷达图像舰船检测。最后,在真实SAR数据集进行了测试,与其他经典的舰船检测方法相比,所提算法不仅有效地抑制了海杂波的影响,而且在检测精度上有较大提高。

Airborne sparse flight array SAR 3D imaging based on compressed sensing in frequency domain

In airborne array synthetic aperture radar(SAR), the three-dimensional(3D) imaging performance and cross-track resolution depends on the length of the equivalent array. In this paper, Barker sequence criterion is used for sparse flight sampling of airborne array SAR, in order to obtain high cross-track resolution in as few times of flights as possible. Under each flight, the imaging algorithm of back projection(BP) and the data extraction method based on modified uniformly redundant arrays(MURAs) are utilized to obtain complex 3D image pairs. To solve the side-lobe noise in images, the interferometry between each image pair is implemented, and compressed sensing(CS) reconstruction is adopted in the frequency domain. Furthermore, to restore the geometrical relationship between each flight, the phase information corresponding to negative MURA is compensated on each single-pass image reconstructed by CS. Finally,by coherent accumulation of each complex image, the high resolution in cross-track direction is obtained. Simulations and experiments in X-band verify the availability.

Optimum selection of common master image for ground deformation monitoring based on PS-DInSAR technique

Considering the joint effects of various factors such as temporal baseline, spatial baseline, thermal noise, the difference of Doppler centroid frequency and the error of data processing on the interference correlation, an optimum selection method of common master images for ground deformation monitoring based on the permanent scatterer and differential SAR interferometry （PS-DInSAR） technique is proposed, in which the joint correlation coeficient is used as the evaluation function. The principle and realization method of PS-DInSAR technology is introduced, the factors affecting the DInSAR correlation are analysed, and the joint correlation function model and its solution are presented. Finally an experiment for the optimum selection of common master images is performed by using 25 SAR images over Shanghai taken by the ERS-1/2 as test data. The results indicate that the optimum selection method for PS-DInSAR common master images is effective and reliable.

基于快速异源配准的高分辨率SAR影像海岛区域正射校正

随着高分辨率合成孔径雷达(SAR)卫星的陆续发射,对天气条件多变的海岛区域进行全天候、全时段的高精度观测已变得可行。作为多种遥感应用的关键前置步骤正射校正,依赖于高精度控制点来纠正SAR影像的几何定位误差。然而,在海岛区域获取符合SAR校正要求的人工控制点不仅成本高,且风险大。为了应对这一挑战,该文首先提出了一种光学与SAR异源影像的快速配准算法,然后基于光学参考底图自动提取控制点,实现了海岛区域SAR影像的正射校正。所提出的配准算法分为两个阶段:首先构建异源影像的共性密集特征,然后在降采样后的特征上进行逐像素匹配,避免了异源影像特征点重复性低的问题。为了降低匹配复杂度,引入了海陆分割掩模以限定搜索范围。接着,对初步匹配点进行局部精细匹配,以减少降采样带来的不准确性。同时,引入海岸线均匀采样点以提升匹配结果的均匀性,并通过分段线性变换模型生成正射影像,确保了稀疏岛屿区域的整体校正精度。该算法在多景海岛区域的高分辨率SAR影像上表现出色,平均定位误差为3.2 m,整景校正时间仅需17.3 s,均优于现有多种先进的异源配准与校正算法,显示出其在工程应用中的巨大潜力。

Edge Detection of River in SAR Image Based on Contourlet Modulus Maxima and Improved Mathematical Morphology

To cope with the problems that edge detection operators are liable to make the detected edges too blurry for synthetic aperture radar(SAR)images,an edge detection method for detecting river in SAR images is proposed based on contourlet modulus maxima and improved mathematical morphology.The SAR image is firstly transformed to a contourlet domain.According to the directional information and gradient information of directional subband of contourlet transform,the modulus maximum and the improved mathematical morphology are used to detect high frequency and low frequency sub-image edges,respectively.Subsequently,the edges of river in SAR image are obtained after fusing the high frequency sub-image and the low frequency sub-image.Experimental results demonstrate that the proposed edge detection method can obtain more accurate edge location and reduce false edges,compared with the Canny method,the method based on wavelet and Canny,the method based on contourlet modulus maxima,and the method based on improved(ROEWA).The obtained river edges are complete and clear.

Airport automatic detection in large space-borne SAR imagery

A method to detect airports in large space-borne synthetic aperture radar（SAR） imagery is studied.First,the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori（MAP） estimator based on the heavytailed Rayleigh model.The attention is then paid on the object of interest（OOI） extracted from the large images.The minimumarea enclosing rectangle（MER） of OOI is created via a rotating calipers algorithm.The projection histogram（PH） of MER for OOI is then computed and the scale and rotation invariant feature for OOI are extracted from the statistical characteristics of PH.A support vector machine（SVM） classifier is trained using those feature parameters and the airport is detected by the SVM classifier and Hough transform.The application in space-borne SAR images demonstrates the effectiveness of the proposed method.

基于电磁散射模型的海上舰船双基SAR图像仿真

合成孔径雷达(synthetic aperture radar,SAR)在地海环境遥感和目标探测识别中发挥着重要作用。相对于单基SAR,双基SAR能够通过调整发射机和接收机的角度对场景进行多方位观测,因此开展海上舰船目标双基SAR图像仿真可为非合作目标的特征研究和探测识别提供方法和手段,在目标探测识别方面有着重要意义。为此,本文利用基于面元化的简化小斜率近似(facet-based simplified small slope approximation,FBS-SSA)方法与几何光学/物理光学(geometrical optics/physical optics,GO/PO)混合方法,提出了一种基于电磁散射模型的海上舰船目标复合场景双基SAR图像仿真方法,实现了不同双基角下的海上舰船SAR图像仿真,并分析了雷达飞行方向与舰船角度、发射机和接收机相对位置、极化、海况对雷达图像的影响。结果表明,双基角、极化、舰船朝向都会对双基SAR图像产生较大的影响,因此可以通过获取不同双基角度下的复合场景SAR图像特征从而可以更好地开展舰船识别。此外,SAR图像中的阴影特征也可作为舰船识别的辅助手段。

Detection of ocean internal waves based on Faster R-CNN in SAR images

Ocean internal waves appear as irregular bright and dark stripes on synthetic aperture radar(SAR)remote sensing images.Ocean internal waves detection in SAR images consequently constituted a difficult and popular research topic.In this paper,ocean internal waves are detected in SAR images by employing the faster regions with convolutional neural network features(Faster R-CNN)framework;for this purpose,888 internal wave samples are utilized to train the convolutional network and identify internal waves.The experimental results demonstrate a 94.78%recognition rate for internal waves,and the average detection speed is 0.22 s/image.In addition,the detection results of internal wave samples under different conditions are analyzed.This paper lays a foundation for detecting ocean internal waves using convolutional neural networks.

基于长短路融合及数据平衡的SAR船舶检测算法

针对SAR图像检测船舶任务中的目标小、近岸样本目标检测困难等问题,文章提出一种名为长短路特征融合网络(Long and Short path Feature Fusion Network,LSFF-Net)的船舶检测网络。该网络通过长短路特征融合模块有效协调了大目标与小目标检测,避免小目标特征信息的丢失。网络中应用结构重参数化结构提高了模块学习能力。为了满足多尺度目标检测,加入特征金字塔网络,融合多尺度特征。为了应对近岸样本目标检测,设计数据重分配算法,提高了对近岸样本目标的检测精度。实验结果表明:在公开数据集检测时,算法的平均精度(Average Precision,AP)达到97.50%,优于主流目标检测算法。该方法为提高SAR图像中小目标和近岸样本目标检测精度提供了新的实现方案。