Directional spectrum analysis and statistics obtained from ERS-1 SAR wave images

This work examines ERS-1 (the first European Remote Sensing Satellite) SAR (Synthetic Aperture Radar) water surface wave images over Hualien of Taiwan, indicating that the variation of SAR signals in space domain is similar to in situ wave data's in time domain. Some statistical properties of SAR data are investigated. The Rayleigh distribution function closely corresponds with the histogram of wave heights, but the Gaussian one cannot for water surface displacements. Evidence reveals that SAR wave signals do not respond well to actual ocean waves effectively. As wave spectral analysis of available SAR data reveals, the appropriate sample size of SAR wave image, sampling average, and moving average should be taken carefully to accurately confirm directional power spectra. Moreover, SAR spectra are compared with in situ ones, confirming that peak frequencies correlate well and wave directions approximately agree with each other. Some differences between both spectral shapes remain somewhat unclear and require further study. Nevertheless, in this study, ERS-1 SAR power spectra verified the feasibility of deriving an appropriate dominant wave direction and peak frequency.     

Satellite SAR observation of shallow bottom topography of the east Australia Sea

INTRODUCTIONOne of the interesting oceanographic features detected with SAR is the shallow bottom toPOgraphic feature and bathymetry nearshore. The knowledge on sea bottom toPOgraphy is of vitalimPOrtance not only for shipping, fishery and all kinds of offshore activities, but also for the calibration and validation of morphodynamic models which are currently under development. Traditional bathymetric surveys consume time and cost. SAR remote sensing of oceanography may offeran altern…     

Monitoring soil moisture dynamics using satellite imaging radar in northeastern Jordan

This paper describes how synthetic aperture radar (SAR) data from the European Environmental Remote Sensing (ERS) satellite series were used to derive estimates of near-surface soil moisture for seasonally vegetated and bare soil surfaces in the area of the Jordan Badia Research and Development Programme (BRDP). Data were acquired between March 1995 and April 1998, covering both wet and dry seasons. A surface scattering model was calibrated using field data: first to understand how soil moisture affects the SAR signal and, secondly, to predict the response of the SAR signal to changes in volumetric soil moisture. Good agreement between predicted and observed estimates was obtained. Model inversion allowed soil moisture predictions to be made that were deemed realistic in terms of soil moisture values.     

地形坡度对干涉相位残余点发布的影响

相位解缠是干涉雷达生成ＤＥＭ的重要步骤之一。干涉相位中的残余点使相位解缠变得复杂。本文研究了残余点产生机理,建立了残余点发生概率的解析表达式。结果表明,在噪声存在的条件下,残余点产生不仅与两幅图像之间的相关系数有关,而且受地形坡度的影响。理论分析与模拟实验表明,地形变化愈大,残余点分布加密     

Rectification of Slant Range Imagery Through a Direct Image to Ground Relationship

A theoretical model for describing a direct image to ground relationship for slant range images extracted from synthetic aperture radar imaging satellites (for instance ERS-1 and ERS-2) is formulated. The rigorous orbital model is based on orbital parameters. Transformations and equations describing the relationship are derived.     

SAR系统不确定性可视化表达及其应用

SAR在成像过程中由于受到外界环境干扰和机载本身的不稳定等不确定因素的影响 ,直接影响 SAR图像的使用。因此 ,对这些不确定因素分析是十分重要的。目前 ,对于 SAR不确定性研究主要是基于统计方法 ,但这种方法不便于揭示 SAR不确定性产生的机理 ,以及不确定性可视化表达。本文首先从机理的角度讨论了 SAR成像系统误差传递函数 ,在确定了 SAR系统误差传递函数之后 ,着重讨论了在误差传递函数基础上利用虚拟现实技术和系统仿真技术进行 SAR成像系统不确定可视化研究 ,最后给出可视化表达的应用实例。     

一种增强型YOLOv3的合成孔径雷达(SAR)舰船检测方法

近年来,海战场成为现代战争的主要作战区域之一,舰船目标逐渐成为海上重点监测对象,能否快速准确地识别海战场舰船目标的战术意图,给指挥员的决策提供必要的支持,这关系到一场海上战役的成败.随着合成孔径雷达(synthetic aperture radar,SAR)成像技术的不断发展,大量SAR图像可用于舰船目标检测与识别.利用SAR图像进行舰船目标检测与识别,已经成为重要的海洋应用之一.针对传统SAR图像舰船检测方法准确率较低的问题,本文在YOLOv3的基础上,结合感受野(receptive field block,RFB)模块,提出一种增强型的SAR舰船检测方法.该方法在最近公开的SAR图像舰船检测数据集上平均准确率值达到了91.50％,与原YOLOv3相比提高了0.92％.实验结果充分表明本文提出的算法在SAR舰船的检测中具有较好的检测效果.     

珠江口海域叶绿素a质量浓度SAR反演模型

以珠江口海域的Radarsatt-2全极化SAR数据和海域表层水面叶绿素a质量浓度实测数据为基础,利用微波散射原理及Cloude Pottier理论对SAR图像进行分解,得到平均散射角a、散射熵H及VH、VV、HH、HV等6个参数;采用BP人工神经网络模型建立上述6个参数与叶绿素a质量浓度的数学关系模型,并结合实测数据对叶绿素a质量浓度进行分类。结果表明:当隐含层节点数为9,输入层和隐含层传递函数分别为tansig和logsig,学习速率和动量系数均为0.2时的网络结构对叶绿素质量浓度反演取得了较好的效果,叶绿素a质量浓度实测值与预测值之间的决定系数(R~2)为0.826。将模型应用于不同时期的2幅图像进行验证,效果良好,与实际情况基本相符。     

星载SAR监测海洋溢油污染的初步研究

对星载SAR监测海洋溢油的基本原理进行总结并分析了影响溢油监测的SAR参数及干扰SAR海洋溢油监测的各种因素;对搜集到的星载SAR数据进行处理,并给出中国黄海海域的海上溢油分布的初步统计分析,结果表明该海区的溢油主要分布在海上主要航线附近。     

“龙王”台风SAR遥感研究

利用0519号"龙王"台风SAR遥感图像,结合NCEP/QSCAT混合风场海面风向反演了高分辨的台风海面风速。基于高分辨率的SAR台风海面风场的风速剖面,对台风期间台湾海峡及周边海域海面风场的小尺度特征及变化进行了分析,结果表明,地形对风场特征的形成有显著作用,它导致台风海面风场结构发生变形以及澎湖列岛附近低风速尾流区、台湾岛的中央山脉北端下风面"角流"区和台湾岛西北海岸背风槽(或诱生低压)等现象的形成及台风期间福建省沿海区的大风天气。