Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation patterns.This paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary learning.In the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time autocorrelation.Online dictionary learning is followed to separate real signals from jamming slices.Under the learned representation,time-varying MISRJs are suppressed effectively.Both simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images

Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.

Simulation of Raw Signal for Spaceborne Synthetic Aperture Radar(SAR)

The study on simulation of raw signal for spaceborne SAR aims at producing raw signal to test and evaluate the system and imaging algorithm. The model used for simulation includes a distributed target model, a platform and target geometry model, and a mathematical architecture used for generation of raw echo. Two aspects are stressed, one is the effects of earth ellip soid and attitude errors on radar impulse respense, the other is quick generation of range migration in azimuth frequency domain. Prescribed statistical characteristics of the model account for a realistic speckle of actual image. Finally, examples are given to validate the simulation of raw signal for spaceborne SAR.

An Information Theory Approach to the Data Compression and Imaging System for Synthetic Aperture Radar （SAR）

Synthetic aperture radar (SAR) is portrayed as a multiple access channel. An information theory approach is applied to the SAR imaging system, and the information content about a target that can be extracted from its radar image is evaluated by the average mutual information measure. A conditional (transition) probability density function (PDF) of the SAR imaging system is derived by analyzing the system and a closed form of the information content is found. It is shown that the information content obtained by the SAR imaging system from an independent sample of echoes will decrease and the total information content obtained by the SAR imaging system will increase with an increase in the number of looks. Because the total average mutual information is also used to define a measure of radiometric resolution for radar images, it is shown that the radiometric resolution of a radar image of terrain will be improved by spatial averaging. In addition, the imaging process and the data compression process for SAR are each treated as an independent generalized communication channel. The effects of data compression upon radiometric resolution for SAR are studied and some conclusions are obtained.

Carbon stock estimation by dual-polarized synthetic aperture radar (SAR) and forest inventory data in a Mediterranean forest landscape

Forest ecosystems play a crucial role in mitigating global climate change by forming massive carbon sinks. Their carbon stocks and stock changes need to be quantified for carbon budget balancing and international reporting schemes. However, direct sampling and biomass weighing may not always be possible for quantification studies conducted in large forests. In these cases, indirect methods that use forest inventory information combined with remote sensing data can be beneficial. Synthetic aperture radar (SAR) images offer numerous opportunities to researchers as freely distributed remote sensing data. This study aims to estimate the amount of total carbon stock (TCS) in forested lands of the Kizildag Forest Enterprise. To this end, the actual storage capacities of five carbon pools, i.e. above- and below-ground, deadwood, litter, and soil, were calculated using the indirect method based on ground measurements of 264 forest inventory plots. They were then associated with the backscattered values from Sentinel-1 and ALOS-2 PALSAR-2 data in a Geographical Information System (GIS). Finally, TCS was separately modelled and mapped. The best regression model was developed using the HH polarization of ALOS-2 PALSAR-2 with an adjusted R^(2) of 0.78 (p < 0.05). According to the model, the estimated TCS was about 2 Mt for the entire forest, with an average carbon storage of 133 t ha^(−1). The map showed that the distribution of TCS was heterogenic across the study area. Carbon hotspots were mostly composed of pure stands of Anatolian black pine and mixed, over-mature stands of Lebanese cedar and Taurus fir. It was concluded that the total carbon stocks of forest ecosystems could be estimated using appropriate SAR images at acceptable accuracy levels for forestry purposes. The use of additional ancillary data may provide more delicate and reliable estimations in the future. Given the implications of this study, the spatiotemporal dynamics of carbon can be effectively controlled by forest management when coupled with easily accessible space-borne radar data.

WATERMARKING WITH REFINED PERCEPTUAL MASKING TUNED FOR SYNTHETIC APERTURE RADAR IMAGES

A watermarking scheme designed for remote sensing images needs to meet the same demand of both invisibility as for ordinary digital images. Due to specific perceptual characteristics of Synthetic Aperture Radar(SAR) images, the watermarking algorithms with consideration of Human Vision System(HVS) modeling from optical images give poor performance when applied on SAR images. This paper examines a variety of factors affecting the noise sensitivity, and further proposes a refined pixel-wise masking approach for watermarking on SAR images. The proposed approach is applied on logarithmic transformed SAR images, and has increased the acceptable watermark embedding strength by about 6 dB to 10 dB while achieving the same levels of watermarked image visual quality. Experimental results show that this approach enhanced the perceptual invisibility of watermarking based on wavelet decomposition.

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。

星载SAR在轨成像高效处理系统硬件实现设计

星载合成孔径雷达(Synthetic Aperture Radar,SAR)在轨成像技术是灾害检测、军事侦察等高时效遥感应用场景的关键手段,以现场可编程门阵列(Field-Programmable Gate Array,FPGA)+数字信号处理器(Digital Signal Processing/Processor,DSP)为处理核心是一种搭建星载SAR在轨成像系统的典型方案,在异构算力、能效比、灵活度等方面综合评价良好。但现有的FPGA+DSP系统在算法支持、大粒度处理、片上并行加速、复杂矩阵转置方面仍研究不足,成像性能仍然有较大提升空间。本文对支持大斜视、高分辨成像的非线性调频变标算法(Nonlinear Chirp Scaling Algorithm,NCS)算法进行分析,根据运算复杂度和类型将算法划分为主流程和辅助路程,以此为依据提出NCS算法在FPGA+DSP系统中的异构映射方案;针对多通道快速傅里叶变换(Fast Fourier Transform,FFT)处理后数据存储形式发生变换、难以流水处理的问题,提出基于时频抽取切换的多通道FFT协同处理方法,保证多通道FFT处理高效进行;针对不同粒度、不同并行度场景下转置需求复杂多变的问题,提出基于X-直接存储器访问(X-Direct Memory Access,XDMA)+片上分割转置的通用交叉转置方案。本文采用2片VX690T FPGA和2片FT6678 DSP为核心处理器研制了星载SAR成像板卡,实现了提出的系统设计方案。同时,本文搭建了基于模拟源+地检的验证环境,对条带/扫描/聚束/滑聚/TOPS模式的仿真点阵数据和条带/滑聚模式的实测数据进行处理。点阵数据的二维峰值旁瓣比约为-13.2 dB,二维积分旁瓣比约为-10.1 dB,成像质量良好;以条带模式为例,图像尺寸为32K×16K,基于NCS算法的平均成像时间为7.81 s,成像速度较现有方案大幅提升。

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。

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。

SAR图像飞机目标智能检测识别技术研究进展与展望

合成孔径雷达(SAR)采用相干成像机制,具有全天时、全天候成像的独特优势。飞机目标作为一种典型高价值目标,其检测与识别已成为SAR图像解译领域的研究热点。近年来,深度学习技术的引入,极大提升了SAR图像飞机目标检测与识别的性能。该文结合团队在SAR图像目标特别是飞机目标的检测与识别理论、算法及应用等方面的长期研究积累,对基于深度学习的SAR图像飞机目标检测与识别进行了全面回顾和综述,深入分析了SAR图像飞机目标特性及检测识别难点,总结了最新的研究进展以及不同方法的特点和应用场景,汇总整理了公开数据集及常用性能评估指标,最后,探讨了该领域研究面临的挑战和发展趋势。

面向SAR图像目标分类的CNN模型可视化方法

卷积神经网络(CNN)在合成孔径雷达(SAR)图像目标分类任务中应用广泛。由于网络工作机理不透明,CNN模型难以满足高可靠性实际应用的要求。类激活映射方法常用于可视化CNN模型的决策区域,但现有方法主要基于通道级或空间级类激活权重,且在SAR图像数据集上的应用仍处于起步阶段。基于此,该文从神经元特征提取能力和网络决策依据两个层面出发,提出了一种面向SAR图像的CNN模型可视化方法。首先,基于神经元的激活值,对神经元在其感受野范围内的目标结构学习能力进行可视化,然后提出一种通道-空间混合的类激活映射方法,通过对SAR图像中的重要区域进行定位,为模型的决策过程提供依据。实验结果表明,该方法给出了模型在不同设置下的可解释性分析,有效拓展了卷积神经网络在SAR图像上的可视化应用。

格式塔感知规律在SAR图像中的有效性初探

合成孔径雷达(SAR)图像是当前微波视觉研究领域的重要数据源。计算机视觉以光学视觉规律为理论基础,无法有效解译SAR图像。因此,借鉴人类视觉感知规律和计算机视觉技术,并融合电磁物理规律的微波视觉成为当前微波遥感领域的一个重要研究方向。探索微波视觉的认知基础对于完善微波视觉理论体系至关重要。该文旨在探讨光学感知规律在微波视觉中的有效性,作为完善微波视觉理论的基础尝试。格式塔感知规律是一类经典的视觉理论,常用于描述人类视觉系统对外部光学世界的感知规律,是计算机视觉的认知理论基础之一。在此背景下,该文以SAR图像为研究对象,借鉴认知心理学实验的设计流程,对格式塔感知规律中的感知组合律和感知不变律在SAR图像中的有效性进行初步研究,探索微波视觉的认知基础。实验结果表明,格式塔感知规律不能够直接应用到SAR图像的算法设计中,人类视觉系统从光学世界中总结出的知识概念、视觉规律在SAR图像中表现不佳,未来需要针对SAR图像等微波图像的特点总结相应的微波视觉认知规律。

大幅宽SAR图像嵌入式舰船实时检测系统设计

针对星载或机载高分辨率合成孔径雷达(synthetic aperture radar,SAR)实时成像后的大幅宽SAR图像舰船实时检测的应用需求,传统的基于FPGA+DSP的嵌入式系统很难同时实现SAR成像处理和基于人工智能技术的大幅宽SAR图像舰船实时检测,为此设计了一种基于3U VPX FPGA+GPU架构的大幅宽SAR图像嵌入式舰船实时检测系统;提出了一种基于YOLOv5s的舰船检测模型,采用基于L2-范数稀疏性惩罚的缩放因子控制法进行轻量化,轻量化舰船检测模型的参数量减小了47.39%,计算量减少了18.67%,平均检测精度为0.968;将轻量化舰船检测模型应用于大幅宽SAR图像嵌入式舰船实时检测系统,并针对典型的10 km×10 km的大幅宽图像应用场景,设计开发基于多线程技术和基于GPU的众核并行计算技术的大幅宽SAR图像嵌入式实时检测系统软件;通过公开的SAR数据集进行功能验证和性能评估,该系统能够满足不同分辨率的大幅宽SAR图像舰船实时检测需求。

基于GB-SAR的矿山边坡局部变形演化与失稳预测

为了提高地基合成孔径雷达(ground-based synthetic aperture radar, GB-SAR)边坡监测效果与失稳预测能力,在广东省某石灰石矿山架设地基雷达,开展实地监测试验,结合矿山地质条件与监测数据研究不同类型的岩体变形演化特征,根据变形特征选择位移最大点的监测数据利用速度倒数法进行失稳预测。通过分析典型案例的监测数据来优化速度倒数法的算法,进而对矿山试验数据进行研究。研究结果表明:通过分析雷达三维模型变形图的局部位移差异,能够初步判断边坡岩体的变形破坏方式;适当增大移动平均滤波法的移动时间周期C,能够更早地发现灾害征兆,更好地识别岩体加速变形阶段及临滑阶段。研究结果可为基于雷达的滑坡预测提供参考。

多方位角融合SAR图像中“扇形”旁瓣分析

随着相控阵天线电子扫描能力的提升,多方位角观测成为星载合成孔径雷达(Synthetic Aperture Radar,SAR)发展方向之一,从多维度提高目标信息解译能力。然而,在公开的多方位角融合SAR图像中存在较强的二维“扇形”旁瓣现象,且二维分布特点不同,容易造成目标误判。因此本文针对此旁瓣方向和多方位角观测目标分辨率开展研究。首先,概述了多方位角观测模式和特性,提出了基于CUDA并行加速的快速后向投影(Back Projection,BP)算法,高效率生成多方位角序贯SAR图像,避免耗时的图像配准和几何校正;其次,基于多方位角观测中斜距平面和地距平面的几何投影关系,重点分析了地距平面目标方位向、距离向分辨率和旁瓣方向,揭示了融合SAR图像中“扇形”旁瓣的形成机理,并解释了二维旁瓣特性不同的原因,基于此可反演获取该SAR图像时卫星和观测场景之间的几何关系,以及融合SAR图像数;最后,通过点目标和扩展目标仿真开展实验,结果表明方位斜视观测中,地距平面方位向、距离向分辨率理论分析精度优于0.02 m,旁瓣方向理论分析精度优于1o,验证了所提分析方法的正确性。

Three-dimensional positions of scattering centers reconstruction from multiple SAR images based on radargrammetry

A method and procedure is presented to reconstruct three-dimensional(3D) positions of scattering centers from multiple synthetic aperture radar(SAR) images. Firstly, two-dimensional(2D) attribute scattering centers of targets are extracted from 2D SAR images. Secondly, similarity measure is developed based on 2D attributed scatter centers' location, type, and radargrammetry principle between multiple SAR images. By this similarity, we can associate 2D scatter centers and then obtain candidate 3D scattering centers. Thirdly, these candidate scattering centers are clustered in 3D space to reconstruct final 3D positions. Compared with presented methods, the proposed method has a capability of describing distributed scattering center, reduces false and missing 3D scattering centers, and has fewer restrictionson modeling data. Finally, results of experiments have demonstrated the effectiveness of the proposed method.

Rapid detection to long ship wake in synthetic aperture radar satellite imagery

The maritime administrative department employs synthetic aperture radar (SAR) satellite remote sensing technology to obtain evidence of illegal discharge of ships. If the ship is discharged during navigation, it forms a long dark wake on the SAR image due to the suppression of the Bragg wave by the oil fi lm. This study investigates key techniques for rapid detection of long ship wakes, thereby providing law enforcement agencies with candidate ships for possible discharge. This paper presents a rapid long ship wake detection method that uses satellite imaging parameters and the axial direction of the ship in images to determine the potential detection area of the wake. Then, the threshold of long ship wake detection is determined using statistical analysis, the area is binarized, and isolated points are removed using a morphological filter operator. The method was tested with ENVISAT Synthetic Aperture Radar and GF-3 SAR data, and results showed that the method was eff ective, and the overall accuracy of the decision reaches 71%. We present two innovations;one is a method that draws a Doppler shift curve, and uses the SAR imaging parameters to determine the detection area of the long wake to achieve rapid detection and reduce the image detection area. The other is where a classical linear fitting method is used to quickly and accurately determine whether the detected dark area is a long ship wake and realizes the twisted long ship wake detection caused by the sea surface flow field, which is otherwise diffi cult to detect by the traditional Radon and Hough transform methods. This method has good suppression performance for the dark spot false alarm formed by low speed wind region or upward flow. The method is developed for maritime ship monitoring system and will promote the operational application of maritime ship monitoring system.

轻小型SAR卫星平飞模式设计

对于轻小型合成孔径雷达(SAR)卫星在轨使用时对重点区域多目标观测效能提升的需求,提出轻小型SAR卫星平飞模式设计方法。介绍了卫星平飞模式设计方法,提出载荷平面相控阵天线距离维大角度扫描能力设计;针对区域多目标,提出条件触发方式目标决策策略,以及星上雷达工作参数计算策略;基于建立的目标决策和任务规划模型,分别针对卫星左侧视、在侧视、平飞、左右机动侧视等4种工作模式下进行多目标观测效能评估仿真分析。结果表明:相比于卫星斜飞模式下,平飞模式目标观测效能可提升至41%;相比于卫星左右机动侧视模式下,平飞模式目标观测效能可提升至73%。因此,设计的卫星平飞模式可有效提升面向区域多目标的观测效能。