高分辨率遥感影像正射纠正的DEM修复技术

介绍了高分辨率商用卫星遥感影像正射处理的一般原理及方法,对不同的布点方案下的DOM纠正精度进行分析,以提高正射影像生产的精度及生产效率。同时,针对DOM现势性不够以及特殊区域的扭曲变形问题,探索进行DEM技术修复的方法,从而提高DOM产品的成图质量,在DOM生产中具有重要的实际应用价值。     

卫星遥感影像的区域正射纠正

针对大区域卫星影像正射纠正所面临的问题,提出了利用平面平差的方法来求解卫星影像定向参数然后进行区域正射纠正的策略。该方案能够保证大区域卫星影像正射纠正后绝对定位精度以及相邻影像接边处相对定位精度的一致性。通过资源三号测绘卫星正视全色影像的实验表明,仅利用少量平面精度为5m的地面控制点,大区域影像经过平面平差后独立检查点的平面精度优于7m,满足了我国1∶5万地形图的精度要求。此外,利用平面平差后每景影像的定向参数进行区域正射纠正,相邻影像接边处连接点的像方精度优于0.5个像素,达到了几何上无缝镶嵌的程度。     

山地、高山地卫星影像的正射处理与质量控制

论述了对山地、高山地大高差情况下卫星影像正射纠正模型的选用、点位纠正与配准进行质量控制和精度分析;对数据处理重采样、分辨率融合、多景影像数字镶嵌方式进行探讨。     

山地地区高分卫星影像正射纠正

随着国内外对地观测卫星技术的不断进步,山地地区高分卫星影像获取能力大幅提升。针对山地区域高分卫星影像云雾多、成像阴影多、纠正位置精度控制难等问题,该文提出山地区域高分卫星正射纠正生产方案。试验证明,经过大气校正、可视化的地形修复等处理后可解决薄雾去除和影像扭曲变形等问题;同时,GPU并行计算可实现影像快速融合、控制点自动选择等操作,提高生产效率,可为山地区域高分卫星影像快速处理提供参照。     

高分辨率遥感影像纠正处理

随着高分辨率遥感卫星日益普及,其应用面也在不断地扩大.城市高分辨率正射影像图以其分辨率高、直观、生产更新周期短等优点,越来越受到城市管理、规划建设部门的青睐,同时也被广泛使用.本文结合重点城市1:10 ooo DLG更新项目,在波段组合、融合处理、正射纠正等方面进行了探讨.     

基于ERDAS软件对QuickBird影像的正射纠正

对于普通影像纠正,传统的纠正模式是多项式纠正,对于高分辨率卫星遥感影像.则采取高精度的纠正方式.本文主要针对QuickBird影像,基于ERDAS软件,采用正射纠正,其校正模型加入了RPC轨道参数和DEM高程模型,从而大大提高了几何精度.     

高分辨率SAR影像正射纠正试验

针对合成孔径雷达侧视成像几何畸变引起的透视收缩、迭掩、阴影等特有现象的问题,该文从星载合成孔径雷达的成像机理出发,利用高精度的星历数据,基于距离-多普勒(R-D)模型和地球模型对合成孔径雷达影像进行正射纠正试验。它的优点是只需要少量控制点对卫星轨道参数进行校正便能获得较高的定位精度。根据这种方法,该文利用一景TerraSAR影像进行试验:使用5个控制点校正轨道参数后,无DEM支持正射纠正后影像的平面精度能达到45m,在90m格网的SRTM支持下平面精度达到7m。试验结果证明,该文所采用的对TerraSAR影像的正射纠正算法是可行的,具有较好的应用前景。     

面向对象的高分辨率卫星影像道路信息提取

本文采用面向对象方法对高分辨率卫星影像道路信息提取。首先加入建筑物矢量数据对影像分割提取出建筑物,然后采用多尺度进行分割,对分割后的对象进行最近邻采样,得到总体分类图。最后根据道路特点构建道路知识库对道路信息优化。试验表明,面向对象的道路信息提取克服了"椒盐现象",取得了较好的提取效果。     

The georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy

RASAT Earth Observation Satellite is the second remote sensing satellite of The Scientific and Technological Research Council of Turkey (TUBITAK) Space Technologies Search Institute (TUBITAK Space). Generally, the first step to utilize the satellite imagery in GIS applications is the accurate geometric correction of the imagery. But, the geometric correction process of RASAT images is somewhat difficult due to insufficient orbit data and lack of satellite imagery processing software with RASAT model. Although the geolocation of RASAT images is investigated in some recent studies, subpixel accuracies cannot be achieved. In this study, different geometric correction methods and combination of them are tested with a more interactive workflow that uses the results of other approaches. Results show that these approaches can be used for the geometric correction of RASAT like pushbroom satellite images with insufficient orbit data and better geolocation accuracies can be achieved by different geometric correction approaches.     

Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method

Interest in high-resolution satellite imagery (HRSI) is spreading in several application fields, at both scientific and commercial levels. Fundamental and critical goals for the geometric use of this kind of imagery are their orientation and orthorectification, processes able to georeference the imagery and correct the geometric deformations they undergo during acquisition. In order to exploit the actual potentialities of orthorectified imagery in Geomatics applications, the definition of a methodology to assess the spatial accuracy achievable from oriented imagery is a crucial topic.In this paper we want to propose a new method for accuracy assessment based on the Leave-One-Out Cross-Validation (LOOCV), a model validation method already applied in different fields such as machine learning, bioinformatics and generally in any other field requiring an evaluation of the performance of a learning algorithm (e.g. in geostatistics), but never applied to HRSI orientation accuracy assessment.The proposed method exhibits interesting features which are able to overcome the most remarkable drawbacks involved by the commonly used method (Hold-Out Validation — HOV), based on the partitioning of the known ground points in two sets: the first is used in the orientation–orthorectification model (GCPs — Ground Control Points) and the second is used to validate the model itself (CPs — Check Points). In fact the HOV is generally not reliable and it is not applicable when a low number of ground points is available.To test the proposed method we implemented a new routine that performs the LOOCV in the software SISAR, developed by the Geodesy and Geomatics Team at the Sapienza University of Rome to perform the rigorous orientation of HRSI; this routine was tested on some EROS-A and QuickBird images. Moreover, these images were also oriented using the world recognized commercial software OrthoEngine v. 10 (included in the Geomatica suite by PCI), manually performing the LOOCV since only the HOV is implemented.The software comparison guaranteed about the overall correctness and good performances of the SISAR model, whereas the results showed the good features of the LOOCV method.     

高分辨率卫星影像车辆检测的抗体网络

将车辆检测的过程视为一种“抗体”检测“危险抗原”的过程, 其中车辆是“危险抗原”, 车辆检测模板是“抗体”。利用一些车辆图像作为训练样本, 建立一种抗体网络学习并获取一组优化的“抗体”。这些“抗体”经过与待测影像的匹配, 实现对道路车辆目标的有效提取。采用0.6m分辨率的QuickBird全色数据进行实验, 实验结果验证了该方法的有效性和可行性。     

Precision rectification of high resolution satellite imagery without ephemeris data

The huge capability of high resolution satellite imageries (HRSI), that includes spatial, spectral, temporal and radiometric resolutions as well as stereoscopic vision introduces them as a powerful new source for the Photogrammetry, Remote Sensing and GIS communities. High resolution data increases the need for higher accuracy of data modeling. The satellite orbit, position, attitude angles and interior orientation parameters have to be adjusted in the geometrical model to achieve optimal accuracy with the use of a minimum number of Ground Control Points (GCPs). But most high resolution satellite vendors do not intend to publish their sensor models and ephemeris data. There is consequently a need for a range of alternative, practical approaches for extracting accurate 2D and 3D terrain information from HRSI. The flexibility and good accuracy of the alternative models demonstrated with KFA-1000 and the well-known SPOT level 1A images. A block of eight KFA-1000 space photos in two strips with 60% longitudinal overlap and 15% lateral sidelap and SPOT image with rational function, DLT, 2D projective, polynomials, affine, conformal, multiquadric and finite element methods were used in the test. The test areas cover parts of South and West of Iran. Considering the quality of GCPs, the best result was found with the DLT method with a RMSE of 8.44 m for the KFA-1000 space photos.     

提高卫星三线阵CCD影像空中三角测量精度及摄影测量覆盖效能

利用数字模拟方法,进一步探讨了类似MOMS-02参数的卫星三线阵CCD影像单航线、航线首末4角隅设一个控制点(以下简写为单航线4控点)的空中三角测量高程精度低的问题。研究得出,宽高比特别小(1:9)只是原因之一,更主要的因素还在于平差整过程数学关系带有近似性(包括EFP法和定向片法)。提出了改善精度的措施,并拟订了提高卫星三线阵CCD影像空中三角测量精度及摄影测量覆盖效能的系统。模拟计算表明,航线长度可以≥2B,在有外方位元素或无外方位元素少量控制点条件下,不论二线交会区,还是三线交会区均可达到高程精度为6m的摄影测量成果。     

On RPC model of satellite imagery

IntroductionThe rational polynomial coefficient ( RPC)model is a generalized sensor model that is usedas an alternative solution for the rigorous sensormodel for IKONOS of the spacei maging. As thenumber of sensors increases along with greatercomplexity ,…     

On RPC model of satellite imagery

The RPC model has recently raised considerable interest in the photogrammetry and remote sensing community. The RPC is a generalized sensor model that is capable of achieving high approximation accuracy. Unfortunately, the computation of the parameters of RPC model is subject to the initial of the parameter in all available liteature. An algorithm for computation of parameters of RPC model without initial value is presented and tested on SPOT-5, CBERS-2, ERS-1 imageries. RPC model is suitable for both push-broom and SAR imagery.     

集群环境下星载线阵相机成像光线追踪模拟

为了精确模拟卫星的成像效果,服务于卫星指标参数论证以及地面系统设计,该文在分析常用卫星模拟方法的基础上,基于多核SMP集群架构,选择利用光线追踪原理,同时依据星载TDI-CCD线阵相机的特有成像模型,通过使用时间-空间积分方法对卫星成像过程进行模拟,得到了高分辨率的星载线阵相机模拟影像。实验结果表明该方法能有效地模拟卫星遥感影像的成像过程,其模拟影像数据可以应用于遥感成像卫星前期指标论证设计中。     

基于Contourlet变换的高空间分辨率遥感影像检索研究

本文在高空间分辨率遥感影像纹理特征提取研究中引入Contourlet变换,将不同尺度、不同方向子带系数矩阵的均值、方差和能量作为纹理特征,并为分类能力强的特征量赋予较大的权值,体现相应子带分类能力的差异性。以QuickBird影像为样本数据、采用五叉树分解策略的纹理检索实证研究表明,基于Contourlet变换提取高空间分辨率遥感影像纹理特征信息的效果优于Gabor小波变换。