ALOS PALSAR影像地球椭球地理编码方法

ALOS PALSAR已经在轨运行1年多,由于其L-波段多极化观测能力,特别适合森林资源调查及生态环境相关遥感应用.然而,如何进行PALSAR数据的地理编码处理是实际应用中需要解决的一个关键问题.本文基于距离-多普勒(RD)模型发展了PALSAR Level-1.1影像产品的地球椭球地理编码校正(GEC)方法,重点论述了从PALSAR CEOS格式元数据记录中提取距离向方程、RD方程参数的具体方法.以一景已经经过几何精校正的Landsat ETM 影像为参考,获取了10个控制点,定量评价了本文所发展的GEC方法的有效性.评价结果表明:东西向均方根误差为32.529m,南北向均方根误差为32.527m,误差约是ETM 影像像元大小(28.5m)的1. 14倍.本文针对PALSAR Level-1.1提出的GEC方法是进一步发展PALSAR其他数据产品的GEC和地形校正地理编码(GTC)方法的基础,对于PALSAR数据的定量化应用及其地理编码处理软件的开发具有重要的参考价值.     

分析法和数值解算法相结合的星载SAR直接定位算法

星载合成孔径雷达（SAR）直接定位算法是发展SAR影像椭球表面校正地理编码（GEC）和地形校正地理编码（GTC）算法的基础。发展高精度的直接定位算法对促进SAR影像的应用具有重要意义。本文在距离-多普勒（RD）定位模型定位算法的基础上，发展了一种将纯分析法（AGM）和纯数值计算法相结合的算法（AIRGM）。新的定位算法兼有纯分析法和纯数值计算法的优点。利用一景ERS-2SARSLC数据，采用两种精度评价方法，将所发展的算法与现有的两种算法的定位精度进行了对比。第1种精度评价方法以影像产品中提供的四角点大地坐标为参考，第2种方法以从1：5万地形图上获取的14个控制点为参考。评价结果表明AIRGM算法不仅定位精度较高，而且执行效率比纯粹的数值解算法（ASF）有了很大的提高。     

星载SAR遥感影像地形辐射校正——以ALOS PALSAR为例

针对地形起伏造成的SAR图像辐射畸变等问题,在分析SAR成像机理及几何结构的基础上,通过推导,构建了基于规则化散射因子及局部入射角的地形辐射校正模型。以ALOS PALSAR为例,依据DEM与卫星轨道参数,采用R-D定位模型及Muhleman半经验模型,在模拟SAR影像的基础上,建立了真实SAR与DEM之间的映射关系,实现了对原始SAR影像的地理编码,提取了局部入射角、投影角及规则化因子等,进行了地形辐射校正,从而消除了面积效应及地形起伏造成的畸变问题。校正后的图像纹理特征更加均匀,散射系数与局部入射角的线性相关性显著降低。该研究对雷达图像的应用具有一定的借鉴意义。     

TerraSAR图像无控制定位精度研究

针对星载TerraSAR图像无地面控制点情况下的目标定位问题,在分析利用卫星星历数据的基础上,结合SAR距离模型和地球椭球模型,提出了一种TerraSAR图像的地面目标直接定位方法。通过杭州地区数据实验结果表明,TerraSAR图像在平均高程60m的丘陵地区,无控制距离向和方位向的定位精度中误差分别达到24.588m和12.265m。     

基于已有控制资料的正射影像自动更新

高分辨率遥感影像作为地理国情监测的基础数据,其新数据获取后的变化检测与适时更新是地理国情实现动态监测的关键。该文将已有的正射影像和数字高程模型作为新获取高分辨率卫星影像的控制资料,结合通用成像模型的有理多项式模型,首先实现高分辨率卫星影像与已有DOM/DEM之间的特征提取和匹配,然后利用RFM模型的区域网平差对匹配结果进行粗差剔除,从而实现高分辨率卫星影像的对地定位,最终实现正射影像更新。实验证明该方法可提高匹配精度与定向参数的可靠性。     

基于SRTM DEM和Landsat TM数据的SAR图像自动地理编码

合成孔径雷达(SAR)图像特殊的斜距成像方式使得利用传统的校正方法很难校正雷达图像。常用的方法是利用DEM和SAR成像几何信息模拟的SAR图像对真实SAR图像进行校正。在区域制图中,平坦地区由于没有地形起伏特征,使得模拟SAR图像特征不明显,无法建立与真实SAR图像的配准关系。本文在研究大范围SAR图像自动化地理编码的过程中,分析讨论了图像模拟和图像匹配的原理和过程,实现了自动判断模拟SAR图像与真实SAR图像的匹配偏移量是否达到信噪比要求,从而自动判断是否使用具有地物专题信息的TM图像代替模拟SAR图像和真实SAR图像进行匹配,实现了海量SAR图像地理编码的自动化。     

星载SAR影像的定位与几何纠正

高精度几何纠正算法对SAR影像的广泛应用具有重要意义,而SAR影像纠正中的一个重要问题是建立影像定位模型。在阐述了距离多普勒定位模型的基础上,利用一景高分辨率的TerraSAR-X影像进行了地理定位实验,实验结果表明采用2次参数方程和3次参数方程描述卫星轨道位置所得的定位精度相差不大,且都能满足精度要求,为了计算方便和减少控制点的数目,通常可以采用二次参数方程来描述卫星位置。定位参数解算完成后,又采用了间接纠正法对影像进行了几何纠正,得出纠正精度不超过3个像素,表明该算法合理可行。     

地理信息库支持下的SAR 影像信息提取

在计算机辅助下如何快速的对整景高分辨率SAR影像进行信息提取已成为一个研究热点。将这一问题构建为一个系统工程来进行考虑解决。在SAR影像中, 许多目标例如河流、湖泊、主要道路、街区和居民地等地理属性信息在短期内不易发生变化, 因此可将这些地理数据集作为先验信息存于数据库中, 在随后对SAR影像的信息处理中, 可以通过预先建立的地理索引调用相应的地理信息来指导信息提取过程, 从而加快整个系统的处理进度, 满足实际工作的需求。     

面向土地利用类型识别的高分辨率SAR数据复合技术研究

2007年以来,相继发射了3颗1m/3m高分辨率SAR卫星,极大地丰富了土地利用动态遥感监测数据源。SAR图像增强是土地利用动态遥感监测必要的预处理步骤。本文针对直接基于SAR数据进行土地利用类型识别中存在的问题,提出了SAR与光学图像、多时相SAR图像、多极化SAR图像合成和单极化SAR图像彩色合成等4种影像复合方法,分析评价了SAR与光学图像融合的应用效果,研究结果可为高分辨率极化SAR数据业务化应用提供技术参考。     

对星载SAR地理编码图像若干评估指标的分析与研究

对SAR图像地理编码过程和描述地理编码图像特性若干评估指标进行了初步的讨论和探究,通过分析一幅Radarsat-1卫星地理编码图像定位精度、指向误差精度、比例误差、扭曲误差等指标,为星载SAR地理编码图像判读解译和应用提供参考。     

ASTER Level 1A数据产品文件解析及应用

深入解析了ASTER数据的结构,研究如何正确读取数据中卫星的位置、速度、时间、姿态角、姿态变化率等与影像定位有关的数据,并研究了这些数据的变化规律。根据ASTER数据的特点,给出了该数据在辐射校正及影像定位方面的应用方法,使得影像能更好的在这些方面得到应用。     

ASTER影像基于卫星轨道和姿态的绝对定位模型

ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer)影像具有较高的地面分辨率,能在可见光、近红外、短波红外、热红外的光谱范围内的14个通道内成像,并且具有立体观测能力,因此AS-TER影像具有广泛的用途,能用于陆地、海洋、冰川、大气等方面的研究。影像定位技术是摄影测量学的核心理论基础之一,是实现从影像坐标到地面坐标转化的关键环节。对遥感图像的应用而言,确定目标位置是非常重要的。本文建立了ASTER影像的定位模型,并以此模型为基础进行ASTER影像定位误差的定量分析,实现在没有控制点和稀少(小于4个)控制点的情况下对ASTER L-1A级影像进行定位。     

Geo-location driven image tagging via cross-domain learning

With the rapid development of location-based social network, more and more multimedia data are uploaded by users. These data always include large-scale of independent information with both textual and visual contents. To bridge the semantic gap in between, we propose a novel cross-domain learning method for automatic image annotation with geo-location information. First, we propose the topic model-based method for popular concept extraction to adaptively construct cross-domain datasets. Then these concepts are utilized to collect the visual correlation information from Flickr. Finally, we leverage cross-domain learning method for model learning. The comparison experiments on cross-domain datasets are conducted to demonstrate the superiority of the proposed method.     

Generation of geometrically and radiometrically terrain corrected SAR image products

Terrain undulations affect the geometric and radiometric quality of synthetic aperture radar images. The correction of these effects becomes indispensable when quantitative image analysis is performed with respect to the derivation of geo- and biophysical parameters. The paper presents a rigorous approach for geometric and radiometric correction of SAR images. Using a digital elevation model, the imaging geometry is reconstructed and is used to perform geometric and radiometric correction of terrain induced distortions. The importance of a stringent radiometric correction based on the integration of the image brightness is emphasized. The approach guarantees that the energy contained in the image data is preserved throughout the geocoding process. The resulting backscattering images are fully terrain corrected and can be used for further quantitative investigations and may also improve qualitative studies as e.g. land cover classifications. The technique is applicable for different sensor types and image products, including already geocoded SAR images. The effect of different resolutions of digital elevation models used for the correction of the backscattering coefficient is investigated.     

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.     

Geocoding RISAT-1 MRS images using bias-compensated RPC models

The purpose of this paper is two-fold. First, the use of the rational polynomial coefficients (RPCs) model is studied for geocoding of Medium Resolution Scan (MRS) ground range (GR) images from the RISAT-1 SAR mission. As the GR images are obtained after many preprocessing image corrections for topographic effect, range cell migration, etc., the number of ground control points (GCPs) required for orthorectification to meet desired geometric quality needs to be established. This assumes importance due to difficulty in visual identification of the GCPs in 18 m-resolution MRS SAR images. Second, three possible methods of bias-compensated RPC models are studied for geocoding. These cases are (A) modified RPC with shift bias, (B) regenerated RPC with shift bias, and (C) regenerated RPC with affine transform model. Experiments are carried out with a set of eight scenes acquired over planar regions especially to avoid the impact of SAR-specific geometric effects such as foreshortening and layover. Geometric accuracy of the orthoimages obtained from these cases is verified at GCPs used for processing as control points and at new GCPs used as check points. It is observed that the modified RPC with the shift bias case required more GCPs to meet the desired geopositioning accuracy. Even though both the regenerated RPC models have shown near similar performance, the regenerated RPC with shift bias compensation is found to reach the required geopositioning accuracy with least number of the GCPs, suggesting it as a strong candidate for realizing operational high precision RISAT-1 geocoded products for multi-temporal data analysis.     

卫星遥感及图像处理平台发展

航天科技是国家综合国力和科技实力的重要体现,而卫星遥感则是航天科技转化为生产力最直接、最现实的途径之一。遥感数据获取与分发、数据处理与信息提取是卫星遥感应用的两个基本步骤。随着国家民用空间基础设施规划中的遥感卫星体系稳步推进,以及商业卫星遥感的蓬勃发展,我国的卫星遥感数据获取能力呈现质量齐升之势。但同时,作为卫星遥感应用的基础设施和关键工具,遥感图像处理系统平台逐渐成为制约自主卫星数据应用和空间信息业务发展的重要因素之一。本文围绕卫星遥感对地观测主题,从卫星遥感数据获取能力、卫星遥感数据处理系统平台两方面,对国内外现状进行综述,在此基础上分析了卫星遥感的发展趋势。     

Near real-time geocoding of SAR imagery with orbit error removal

When utilizing knowledge of the spacecraft trajectory for near real-time geocoding of Synthetic Aperture Radar (SAR) images, the main problem is that predicted satellite orbits have to be used, which may be in error by several kilometres. As part of the development of a Dutch autonomous mobile ground station to receive and process satellite SAR data, a method has been developed that removes the effects of orbit errors from the computed azimuth and slant-range coordinates, whilst maintaining the real-time character. Results show that predicted orbits can be used to geocode SAR imagery in near real time with the same accuracy as when using precise orbits, i.e. 30–40m.     

Multisource ERS-1 and optical data for vegetal cover assessment and monitoring in a semi-arid region of Algeria

This paper highlights advantages of using Synthetic Aperture Radar (SAR) data combined with multispectral data to improve vegetal cover assessment and monitoring in a semi-arid region of southern Algeria. We present a number of preprocessing and processing techniques using multidate optical data analysis alone and SAR ERS-1 and Landsat Thematic Mapper (TM) data integration due to aspects of radar image enhancement techniques and the study of roughness of different types of vegetation in steppic regions. Image data integration has become a valuable approach to integrate multisource satellite data. It has been found that image data from different spectral domains (visible, near-infrared, microwave) provides datasets with complementarity information content and can be used to improve the spatial resolution of satellite images. In this communication, we present a part of the cooperation research project which deals with fusing ERS-1 SAR geocoded images with Landsat TM data, investigating different combinations of integration and classification techniques. The methodology consists of several steps: (1) Speckle noise reduction by comparative performance of different filtering algorithms. Several filtering algorithms were implemented and tested with different window sizes, iterations and parameters. (2) Geometric superposition and geocoding of optical images regarding SAR ERS-1 image and resampling at unique resolution of 25 m. (3) Application of different numerical combinations of integration techniques and unsupervized classifications such as the Forgy method, the MacQueen method and other methods. The results were compared with vegetal cover mapping from aerial photographs of the region of Foum Redad in the south of the Saharian Atlas. The combinations proposed above allow us to distinguish different themes in the arid and semi-arid regions in the south of the Saharian Atlas using a colour composite image and show a good correlation between different types of land cover and land use and radar backscattering level in the SAR data which corresponds essentially to the roughness of the soil surface.