天山地区SAR数据雪盖制图研究

在分析合成孔径雷达（ＳＡＲ）成像机理及雪盖参数散射特性的基础上，利用航天飞机搭载的成像雷达（ＳＩＲ－Ｃ）获取的天山Ｃ波段、多极化ＳＡＲ数据，进行提取雪盖信息的实验。数据通过预处理、散射系数图像的计算、去噪、入射角改正等处理后，利用ＢＡＹＥＳ监督分类方法产生该地区的雪盖分类图。实验结果表明，利用多波段、多极化ＳＡＲ进行雪盖制图是可行的。     

应用极化雷达估算农作物覆盖地区土壤水分相对变化

通过应用一阶离散植被模型,结合前人研究成果及雷达极化特性提出了多时相多极化雷达后向散射消除农作物覆盖层影响的算法:①应用已知的假设关系将植被层的体散射用交叉极化的总散射代替;②分析并将垂直极化的总后向散射中贡献很小的植被-土壤多次散射忽略;③将直接地表的后向散射分解成土壤水分与地表粗糙度的函数,使用重轨数据消除了地表粗糙度和农作物覆盖层的影响,并使用多时相全极化L波段(频率为1.2GHz)机载雷达测量数据进行验证,成功的估算了地表土壤水分的相对变化。     

成像雷达(SAR)遥感地质应用综述

成像雷达（SAR）遥感以其独有的全天时、全天候观测能力和对地表的穿透性及形态探测能力，特别是现在新型成像雷达遥感技术的发展，使之在地质学应用中比光学遥感更具优势。文章结合SAR应用技术的发展过程，即由单波段单极化到多波段多极化，再发展到现在极化测量和干涉测量阶段，综述了成像雷达遥感在地学中的应用，特别对新型成像雷达技术（极化雷达、差分干涉雷达）在岩性分类、探测地震区域形变的地学应用作了实例介绍。     

典型地物后向散射特性的测量与分析

利用S和C波段FM-CW制式陆基微波散射计测量了不同极化、不同入射角以及不同方位向和不同时间的典型地物后向散射系数,包括裸土、冻土、草地和玉米等。首先介绍了实验装置和测量方法,根据不同的散射机制,将地物分为以面散射为主和以体散射为主两大类并进行了详细的分析:包括各种地物在不同入射角、不同极化状态和不同时间的散射特性,以及产生这些差异的原因;同时还结合相应的地物散射模型,定量研究了后向散射系数随地表参数的函数关系,反演得到了不同地物的地表参数,并对比实测数据分析了各种影响因素。     

PALSAR及RADARSAT2全极化雷达数据在地质构造应用中的研究

由于雷达数据不受天气影响,且具有一定的穿透能力,近年来已被广泛的运用在地质构造信息提取研究中。ALOS PALSAR与RADARSAT2是近年发展的最先进的雷达传感器,其全极化模式同时又具有较高的空间分辨率,这为植被区地质构造研究提供了良好的数据源。本文选取非洲埃塞俄比亚西部为研究区,通过对研究区域两种全极化雷达数据进...     

全极化探地雷达地下管道分类识别技术

常规探地雷达大多数是单极化雷达,单极化雷达只能获得单极化数据,对复杂环境中管道准确快速地识别比较困难。为了解决此问题,本文采用了全极化探地雷达识别管道的方法,提取了单一管道目标、多个管道目标中任何一个管道目标和受其他目标影响的管道目标的极化属性。结果表明,全极化探地雷达技术对处于极化属性受到影响环境下的管道目标均能较好地识别。因此,全极化探地雷达能够获得更加全面的目标体极化信息,有效地解决了复杂环境中管道准确快速识别比较困难的问题。     

GNSS-R陆面遥感中反射信号的极化特性研究

GNSS-R是利用导航卫星的反射信号对海面或者陆面进行遥感的一种成本低、功耗小、时空分辨率相对较高的新型遥感手段。从机理上讲GNSS-R为双站雷达,以微波电磁散射模型为研究基础,对于右旋圆极化发射,右旋圆极化(RHCP)、左旋圆极化(LHCP)、H极化和V极化接收时,裸土和植被的散射特性进行了理论模拟。裸土散射特征的模拟采用镜像反射率模型和物理光学模型(PO)。对于植被部分,则采用镜像模型Spec-mimics(Specular-mimics)。在后向散射模型Mimics的基础上,加入散射角度(天顶角和方位角),同时修改由于散射角度加入而导致的相位矩阵、消光矩阵和地表反射矩阵、散射矩阵等,可以得到双站散射模型Bi-mimics。在其基础上,将角度设置为镜像,得到Spec-mimics模型。模拟结果表明,在10°～70°的入射角范围内,接收为LHCP极化时,只有在大的入射角(小接收机仰角)下会有散射值。线极化(H极化和V极化)则在整个角度范围内都有响应。而V极化在此角度范围内动态响应最大。因此如果在小入射角时,采用LHCP天线接收地表反射信号,极化损失较为严重;线极化在该入射角度范围内均可接收到地表反射信号,V极化由于对角度响应较好,因此更有利于地物特征研究。该理论模拟对接收机天线的极性设计提供了一定的理论指导。     

GNSS-R Land Remote Sensing about Reflected Signal＇ s Polarization Characteristics

GNSS-R是利用导航卫星的反射信号对海面或：著陆面进行遥感的一种成本低、功耗小、时空分辨率相对较高的新型遥感手段。从机理上讲GNSS-R为双站雷达,以微波电磁散射模型为研究基础,对于右旋圆极化发射,右旋圆极化（RHCP）、左旋圆极化（LHCP）、H极化和V极化接收时,裸土和植被的散射特性进行了理论模拟。裸土散射特征的模拟采用镜像反射率模型和物理光学模型（PO）c对于植被部分,则采用镜像模型Spec-mimics（Specular-mimics）。在后向散射模型Mimics的基础上,加入散射角度（天顶角和方位角）,同时修改由于散射角度加入而导致的相位矩阵、消光矩阵和地表反射矩阵、散射矩阵等,可以得到双站散射模型Bi．mimics。在其基础上,将角度设置为镜像,得到Spec—mimics模型。模拟结果表明,在10°～70°的入射角范围内,接收为LI-ICP极化时,只有在大的入射角（小接收机仰角）下会有散射值。线极化（H极化和V极化）则在整个角度范围内都有响应。而V极化在此角度范围内动态响应最大。因此如果在小入射角时,采用LHCP天线接收地表反射信号,极化损失较为严重;线极化在该入射角度范围内均可接收到地表反射信号,V极化由于对角度响应较好,因此更有利于地物特征研究。该理论模拟对接收机天线的极性设计提供了一定的理论指导。     

基于H/α分解原理的古河道信息提取——以松嫩平原西部地区为例

古河道对于重现古气候、古生态环境变化有着重要的意义。极化合成孔径雷达（SAR）数据以散射矩阵的形式记录了地物的后向散射信息,能有效地识别隐伏的古河道信息。本文以古河道发育的松嫩平原西部作为研究区域,选取Sentinel-1双极化数据（VV-VH）作为数据源,通过VV-VH双极化模式下的H/α分解处理,构建了由散射熵H与散射角α构成的二维H/α平面。依据雷达波在古河道充填沉积物中发生体散射以及在古河床底界面发生二次散射,并且体散射功率大于二次散射功率,确定了古河道散射类型属于H/α平面上的高熵多次散射。结合此特征与Sentinel-2影像,最终对研究区内的古河道信息进行了提取。研究表明,通过VV-VH双极化模式下的H/α分解方式可以提取到在Sentinel-2影像上无明显特征的古河道信息。     

利用探地雷达的极化特性检测建筑物结构

探地雷达偶极子天线激发的是线性极化波,其主要极化方向与天线的长轴平行。接收天线对与其长轴平行的入射电场分量最敏感。对于高导圆柱体,当半径与波长之比较小时,其散射场TE极化的后向散射宽度要小于TM极化的后向散射宽度。通过桥梁结构检测实例证明,利用这种特性,在建筑物结构检测时,采用发射天线和接收天线平行、且垂直于钢筋走向的天线布置,可以减少浅部钢筋的散射,增强雷达剖面中对深部目标的辨识。     

航天飞机成像雷达数据在库车冲断带前缘构造分析中的应用

通过新疆库车县北航天飞机TM和SIR-C/X-SAR图像的处理,利用多参数雷达图像对浅覆盖物下隐伏构造的探测能力,从雷达图像上提取研究区低序次的构造信息,以此分析左行走滑活动相关变形产生的正、负花状构造的地面特征。采用分数维分析方法,研究不同冲积物图像表面的粗糙度特征,确定了库车冲断带前缘在库车县北向西端延伸的位置。显示出多波段多极化成像雷达对新构造的识别能力,从而确定库车冲断带前缘的构造特征。     

偏振微波雷达探测大气研究进展及几个问题的考虑

近年国际上偏振微波雷达探测大气研究有了重要进展,成为雷达气象学研究的新热点,并可能成为天气雷达发展的重要方向。介绍了微波圆偏振技术现状和双线偏振测量技术体制的重要进展,以及近年在气象应用中所获得的最新研究结果,展望了偏振探测技术的应用前景。进一步讨论了开展偏振探测技术研究所应考虑的有关偏振类型、实现体制、方案设计、产品算法和波段选择中的问题,对偏振探测技术研究具有参考价值。     

海洋卫星测高技术和海洋地形试验TOPEX卫星计划

ＴＯＰＥＸ卫星是目前精度最高的海洋测高卫星，利用ＳＬＲ技术确定的ＴＯＰＥＸ卫星轨道径向精度达到２．８ｃｍ，这使它可以有效地监测全球的海洋地形。ＴＯＰＥＸ卫星主要用于全球的海面变化和洋流研究。利用ＴＯＰＥＸ资料可以得到新的地球引力场，海洋大地水准面和海潮模型。     

Mapping gossans in arid regions with Landsat TM and SIR-C images: the Beddaho Alteration Zone in northern Eritrea

Massive sulphide deposits in the Neoproterozoic Arabian-Nubian Shield are exposed at the surface as Fe-rich crusts termed gossans. Gossans are typically a few tens of metres across but are surrounded by wider clay- and Fe-rich alteration zones. Although Fe-rich gossans have characteristic reflectance spectra and surface roughness, they are often too small to be directly detected by Landsat TM or SIR-C images, both of which have about 30 m spatial resolution. In this paper, a procedure is described whereby gossans and the surrounding alteration zones can be identified and mapped by Landsat TM and SIR-C data using the Beddaho Alteration Zone and the Tebih Gossan in northern Eritrea as an example. Clay and Fe alteration index maps were generated by density slicing for Landsat TM band-ratios and , respectively. Landsat 5/7-4/5-3/1 TM images characteristically depict small (tens of pixels) gossans in blue and the more extensive alteration zones in pinkish purple. Chh-LhhLhh/Chh SIR-C images succeeded in identifying the gossan due to enhanced back-scattering of the radar shorter wavelength (6 cm) C-band by the rough gossan surfaces. This enhanced back-scattering might also be partially due to the characteristic dielectric property of the Fe-rich minerals forming the gossans. Choosing known gossans from both 5/7-4/5-3/1 Landsat TM and Chh-Lhh-Lhh/Chh SIR-C images as training sites for supervised classification helped to outline areas with reflectance spectra and radar back-scattering properties similar to those of the training sites. These results show significant correlation between supervised classifications based on the two data sets, suggesting a way to use combined visible and near infrared (VNIR) and radar imagery to explore for mineral deposits in arid regions.     

基于深度特征的双极化SAR遥感图像岩性自动分类

基于像元基元、极化合成孔径雷达（Synthetic Aperture Radar,SAR）数据和传统机器学习算法的岩性分类方法,易受SAR图像固有斑点噪声影响,精度不高.为了降低噪声的影响,本研究以大尺度像元邻域为基元,用于表征地表地质体的遥感图像特征和岩性语义信息;采用高分三号双极化SAR数据进行极化分解构建3通道假彩色合成影像;然后采用深度卷积神经网络（Deep Convolutional Neural Network,DCNN）迁移学习的方法,提取有效的深度特征表示,分别实现5 m和15 m两种空间分辨率下岩性遥感自动分类.结果表明:基于不同分辨率数据和不同DCNN算法,岩性遥感自动分类的总精度均大于80%,最高精度达到91%.基于大尺度像元邻域和DCNN迁移学习方法,能够实现基于SAR数据的高精度岩性分类.      

Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data

This study suggests a novel approach to the retrieval of soil surface parameters using a single-acquisition single-configuration synthetic-aperture radar (SAR) system. Soil surface parameters such as soil moisture and surface roughness are key elements for many environmental studies, including Earth surface water cycles, energy exchange, agriculture, and geology. Remote sensing techniques, especially SAR data, are commonly used to retrieve such soil surface parameters over large areas. Several backscattering models have been proposed for soil surface parameters retrieval from SAR data. However, commonly, these backscattering models require multi configuration SAR data, including multi-polarization, multi-frequency, and multi-incidence angle. Here we propose a methodology that employs single-acquisition single-configuration SAR data for the retrieval of soil surface parameters. The originality is to use single-acquisition single-configuration SAR data to retrieve the soil surface parameters using an optimization approach by the genetic algorithm (GA); we have used the modified Dubois model (MDM) in HH polarization as the backscattering model. Three HH polarization and C band data sets from Quebec (Radarsat-1), Ontario (SIR-C), and Oklahoma (AIRSAR) were analyzed. The retrieved values of soil moisture and soil surface roughness were then compared to ground truth measurements with corresponding parameters. We employed diverse criteria, including the mean absolute error (MAE), the root mean square error (RMSE), the coefficient of performance (CP), and the correlation coefficient to investigate the performance of the proposed methodology. This analysis suggests the capability of the GA for the retrieval of soil surface parameters. Based on our findings, this method presents a viable alternative approach to the retrieval of soil surface parameters when only single-acquisition single-configuration SAR data is available.     

Using remotely sensed data to modify wind forcing in operational storm surge forecasting

Storm surges are abnormal coastal sea level events caused by meteorological conditions such as tropical cyclones. They have the potential to cause widespread loss of life and financial damage and have done so on many occasions in the past. Accurate and timely forecasts are necessary to help mitigate the risks posed by these events. Operational forecasting models use discretisations of the governing equations for fluid flow to model the sea surface, which is then forced by surface stresses derived from a model wind and pressure fields. The wind fields are typically idealised and generated parametrically. In this study, wind field datasets derived from remotely sensed data are used to modify the model parametric wind forcing and investigate potential improvement to operational forecasting. We examine two methods for using analysis wind fields derived from remotely sensed observations of three hurricanes. Our first method simply replaces the parametric wind fields with its corresponding analysis wind field for a period of time. Our second method does this also but takes it further by attempting to use some of the information present in the analysis wind field to estimate future wind fields. We find that our methods do yield some forecast improvement, most notably for our second method where we get improvements of up to 0.29 m on average. Importantly, the spatial structure of the surge is changed in some places such that locations that were previously forecast small surges had their water levels increased. These results were validated by tide gauge data.     

Potentials of alternate polarization of Envisat ASAR data in geological mapping — A case study in Kurnool Group of rocks,Andhra Pradesh

The application of SAR data is a proven technology in geological studies but very few accounts are available in India, which can evaluate and demonstrate the utility of microwave signatures as an important tool for geological mapping. In this connection, the significance of polarization is an important parameter in enhancing geological elements. Present study reveals that the simple polarization composite prepared from different polarization channels can significantly aid the delineation of geological features as demonstrated from the Proterozoic metasedimentary sequences of Kurnool Group. The polarization colour composites reveal that different sedimentary units can be differentiated on the basis of variable back scattering return in different polarization channel. Further geological structures of regional importance can also be delineated in these colour composite images. Comparative analysis of different composite images with published geological maps, illustrates the capabilities of the microwave polarization in enhancing geological elements and how they can be used in updating geological data.