资源三号卫星影像DEM提取与精度分析

为提高国产遥感卫星影像定位精度,提取高精度DEM,该文基于卫星遥感的成像模型和间接平差理论,利用有理函数模型和像面仿射变换模型,构建了卫星影像平差数学模型;利用该数学模型对资源三号卫星立体影像进行了平差处理,建立了研究区域DEM。分析结果表明:DEM在无控制点情况下,平地区域平面精度达到6.12m,高程精度达到8.39m;山地区域平面精度达到6.20m的,高程精度达到8.62m,数据精度能够满足1∶50000比例尺地形图测图标准和要求。研究结果验证了国产卫星数据的定位精度。     

基于稀少控制IKONOS的近海岸岛礁立体影像定向研究

针对我国岛礁控制现状,进行了基于稀少控制IKONOS的近海岸岛礁立体影像区域网平差定向实验,通过对不同控制方案的定向精度分析,提出了稀少控制的岛礁影像控制点布设方案,为我国近、远海重点岛礁的立体影像定向测图提供参考。     

多重观测卫星影像的无控区域网平差

为了保证卫星遥感影像获取地面目标区域信息的准确性,需要对影像数据进行几何校正,几何校正的精度决定着遥感图像应用的效果。常规的校正方法需要地面控制点信息,但在境外、我国西部、荒漠等这些地方很难获取控制点。针对上述问题,提出了多重观测卫星影像的无控区域网平差方法,提高无控定位的精度。与常规区域网平差不同,该方法的误差方程基于"单物方—多像方"的连接点集建立,在无控条件下误差方程能够收敛到更加精确的解。通过误差补偿模型调整每幅影像的有理函数模型系数,使得不同方向的定位误差进行抵消,从而在无控制点的条件下提高遥感影像的定位精度。首先,利用卫星影像的RPC文件和像方误差补偿模型,建立多重重叠的区域网平差模型,然后,利用共轭梯度算法迭代求解误差方程,最后调整RPC系数,提高遥感图像定位精度。通过资源三号卫星影像试验表明:多重观测区域网平差将遥感图像的平面定位精度由19.8m提高至12.9m,能够有效提高影像的几何定位精度。     

近景目标立体影像用于城市三维建模的空间定位

该文利用数字近景摄影测量的方法,对构成立体影像的近景目标序列影像进行数字摄影测量处理,以提取目标模型的三维空间坐标。给出了自由网模型的构建方法和大角度模型拼接的方法,解决了三维空间模型在没有控制点,仅有相对控制条件下,光束法自由网平差解算的空间基准问题。并给了三维网空间定位的实现过程和结论。     

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。     

IKONOS卫星遥感影像的精度分析

介绍了对目前最高分辨率的民用卫星(IKONOS)遥感影像精度分析的方法及结果,并介绍和使用了仿射变换、线性纠正、投影变换和多项式变换等常用的6种遥感图像纠正的方法,对试验区的影像进行了实例测试,并对不同方法所产生的残差进行了分析。计算结果表明,IKONOS卫星遥感影像的分辨率约为地面1.000 m±0.010 m;测试分析还表明,对IKONOS卫星遥感影像的纠正以采用仿射变换的方法为最佳,纠正后的IKONOS影像可以直接用于1∶10 000比例尺地形图的测绘。     

JX—4A全数字摄影测量工作站中IKONOS立体影像的处理方法与应用

介绍了在JX－4A全数字摄影测量工人作站中对IKONOS立体影像处理的理论,方法及处理过程,并以实验结果证实了用IKONOS影像进行立体测图的应用范围和推广前景。     

月球卫星CE-1三线阵影像数据的解算试验

利用"嫦娥一号"卫星上搭载的CCD立体相机获取的月面三线阵影像数据,再配合激光高度计(LAM)数据,经过摄影测量解算可以制作月面数字高程模型(DEM)和数字正射影像图(DOM)。本文采用两种方法对月球卫星CE-1三线阵影像数据进行了月面点坐标的解算试验,方法一是根据测控给出的外方位元素直接解算,方法二是对影像上提取的像点进行区域网平差解算,根据两种方法的解算结果,比较了各方法的精度,达到了预期目的。     

普通相机近景影像空三加密精度分析

为验证利用普通数码相机获取地面近景影像方案的可行性,为后续航空影像与地面近景影像联合解算提供依据,利用重叠度公式和摄影比例尺公式推求本实验普通数码相机的摄影距离、摄影基线与重叠度之间的关系,并利用光束法区域网平差对影像进行平差解算。实验结果表明,利用普通数码相机及该文的摄影方案在地面采集的影像质量可以达到与航空摄影数据进行联合解算的精度要求,并且重叠度和控制点数量增加可以提高空中三角测量精度。     

普通相机近景影像空三加密精度分析

为验证利用普通数码相机获取地面近景影像方案的可行性,为后续航空影像与地面近景影像联合解算提供依据,利用重叠度公式和摄影比例尺公式推求本实验普通数码相机的摄影距离、摄影基线与重叠度之间的关系,并利用光束法区域网平差对影像进行平差解算。实验结果表明,利用普通数码相机及该文的摄影方案在地面采集的影像质量可以达到与航空摄影数据进行联合解算的精度要求,并且重叠度和控制点数量增加可以提高空中三角测量精度。     

Spatial accuracy of orthorectified IKONOS imagery and historical aerial photographs across five sites in China

High‐resolution (?1?m) satellite imagery and archival World War II era (WW2) aerial photographs are currently available to support high‐resolution long‐term change measurements at sites across China. A major limitation to these measurements is the spatial accuracy with which this imagery can be orthorectified and co‐registered. We orthorectified IKONOS 1?m resolution GEO‐format imagery and WW2 aerial photographs across five 100?km² rural sites in China with terrain ranging from flat to hilly to mountainous. Ground control points (GCPs) were collected uniformly across 100?km² IKONOS scenes using a differential Global Positioning Systems (GPS) field campaign. WW2 aerial photos were co‐registered to orthorectified IKONOS imagery using bundle block adjustment and rational function models. GCP precision, terrain relief and the number and distribution of GCPs significantly influenced image orthorectification accuracy. Root mean square errors (RMSEs) at GCPs for IKONOS imagery were <2.0?m (0.9–2.0?m) for all sites except the most heterogeneous site (Sichuan Province, 2.6?m), meeting 1:12?000 to 1:4800 US National Map Accuracy Standards and equalling IKONOS Precision and Pro format accuracy standards. RMSEs for WW2 aerial photos ranged from 0.2 to 3.5?m at GCPs and from 4.4 to 6.2?m at independent checkpoints (ICPs), meeting minimum requirements for high‐resolution change detection.     

Photogrammetric processing of high-resolution airborne and satellite linear array stereo images for mapping applications

This article introduces a mathematical model for photogrammetric processing of linear array stereo images acquired by high-resolution satellite imaging systems such as IKONOS. The experimental result of the generation of simulated IKONOS stereo images based on photogrammetric principles, IKONOS imaging geometry and a set of georeferenced aerial images is presented. An accuracy analysis of ground points derived from the simulated IKONOS stereo images is performed. The impact of the number of GCPs (ground control points), distribution of GCPs, and image measurement errors on the ground point accuracy is investigated. It is concluded that an accuracy of ground coordinates from 2 m to 3 m is attainable with GCPs, and 5 m to 12 m without GCPs. Two data sets of HRSC (high resolution stereo camera) and MOMS (modular opto-electronic multispectral stereo-scanner)-2P are also utilized to test the model and system. The presented data processing method is a key to the generation of mapping products such as digital terrain models (DEM) and digitial shorelines from high-resolution satellite images.     

Photogrammetric exploitation of IKONOS imagery for mapping applications

The launch of IKONOS by Space Imaging opens a new era of high-resolution satellite imagery collection and mapping. The IKONOS satellite simultaneously acquires 1?m panchromatic and 4?m multi-spectral images in four bands that are suitable for high accuracy mapping applications. Space Imaging uses the rational function model (RFM), also known as rational polynomial camera model, instead of the physical IKONOS sensor model to communicate the imaging geometry. As revealed by recent studies from several researchers, the RFM retains the full capability of performing photogrammetric processing in absence of the physical sensor model. This paper presents some RFM-based processing methods and mapping applications developed for 3D feature extraction, orthorectification and RPC model refinement using IKONOS imagery. Comprehensive tests are performed to test the accuracy of 3D reconstruction and orthorectification and to validate the feasibility of the model refinement techniques.     

复小波包域遥感图像局部自适应融合算法

为了使融合后的图像在保持原IKONOS 卫星图像多光谱特性的同时,最大可能地提高图像空间 分辨率,提出了一种基于四树复小波包变换的SAR 图像与多光谱IKONOS 卫星图像相融合的新方法．该方法 利用复小波包变换的多方向性和对高频细节信号良好的时频局部化分析能力,分别对IKONOS 图像经HIS 空 间变化的I 分量子图和SAR 图像进行复小波包分解,并对分解后的低频复系数采用取平均或取大值的方法、 对高频方向复系数采用邻域一致性测度的局部自适应方法进行复系数融合．用融合后复系数经复小波包反变 化得到的图像代替原IKONOS 图像经HIS 变换的I 分量,再经HIS 空间反变换得到最终的融合图像．实验结 果表明,该融合算法在光谱保留和空间质量提高方面,比传统的基于小波变换的融合算法具有更高的性能．     

Geometric accuracy assessment of the orthorectification process from very high resolution satellite imagery for Common Agricultural Policy purposes

This study has, as its main aim, the assessment of different sensor models to achieve the best geometric accuracy in orthorectified imagery products obtained from IKONOS Geo Ortho Kit and QuickBird basic imagery. The final orthoimages are compared, both geometrically and visually, with the panchromatic orthophotos based on a photogrammetric flight with an approximate scale of 1 : 20 000, which are now used for the European Union Common Agricultural Policy in Andalusia (Spain). Two‐dimensional root mean square (RMS_2d) errors in independent check points are used as accuracy indicators. The ancillary data were generated by high accuracy methods: (1) check and ground control points (GCPs) were measured with a differential global positioning system and (2) an accurate digital elevation model was used for image orthorectification. Two sensor models were used to correct the satellite data: (1) a three‐dimensional (3D) rational function refined by the user with zero‐ (RPC0) or first‐(RPC1) order polynomial adjustment and (2) the 3D Toutin physical model (CCRS). For the IKONOS image, the best results in the final orthoimages (RMS_2d of about 1.15 m) were obtained when the RPC0 model was used. Neither a large number of GCPs (more than nine), nor a better distribution of them, improved the results obtained with the RPC0. For the QuickBird image, the CCRS model generated the best results (RMS_2d of about 1.04 m), although it was sensitive to the number and distribution of the GCPs used in its computation.     

Retrieval of leaf area index in different vegetation types using high resolution satellite data

With the successful launch of the IKONOS satellite, very high geometric resolution imagery is within reach of civilian users. In the 1-m spatial resolution images acquired by the IKONOS satellite, details of buildings, individual trees, and vegetation structural variations are detectable. The visibility of such details opens up many new applications, which require the use of geometrical information contained in the images. This paper presents an application in which spectral and textural information is used for mapping the leaf area index (LAI) of different vegetation types. This study includes the estimation of LAI by different spectral vegetation indices (SVIs) combined with image textural information and geostatistical parameters derived from high resolution satellite data. It is shown that the relationships between spectral vegetation indices and biophysical parameters should be developed separately for each vegetation type, and that the combination of the texture indices and vegetation indices results in an improved fit of the regression equation for most vegetation types when compared with one derived from SVIs alone. High within-field spatial variability was found in LAI, suggesting that high resolution mapping of LAI may be relevant to the introduction of precision farming techniques in the agricultural management strategies of the investigated area.     

像素级遥感图像融合并行算法研究与实现

本文针对遥感图像IHS、HPF、DWT等典型的像素级融合算法,提出并实现了相应的基于数据并行的并行融合算法P-IHS、P-HPF、P-DWT,并在算法时空复杂度分析的基础上进行了通信、I/O优化。针对IKONOS卫星遥感图像在机群系统上的测试结果表明,我们提出的并行算法可获得良好的并行加速比,并行效率较高。这三类算法适合于对实时性要求比较高的遥感应用领域。     

Use of IKONOS satellite data to identify informal settlements in Dehradun,India

This research highlights the potential of IKONOS satellite data to identify the temporary structures in a part of Dehradun, India. Houses with plastic roof covers reveal dark grey tone in merged IKONOS product, which helps to extract information about the people living below poverty line. Extent of these areas can be quantified using classification technique successfully. Mixing of shadow pixels with temporary structures poses limitation and need further research.     

基于区块链的星间通信网络安全加密控制系统设计

目前设计的星间通信网络安全加密系统加密深度低,导致通信误码率高,无法保证星间通信网络安全;引入区块链技术设计一种新的星间通信网络安全加密系统;选择性能最优的LEO类型的卫星放置在中层的卫星网络通信轨道中,其他类型的LEO卫星则各个成为单独的卫星网络分体系,处理主体系中的杂乱通信信号;构建地面用户之间的链路关系及卫星网络链路,实现高阶层卫星通过无线电链路或光纤链路对下一阶层的卫星覆盖,完成系统硬件设计;引用区块链分布式数字化身份加密技术,通过用户使用密钥对公钥的加密保护结构图定位通信网络的状态以及通信网络的加密状态,在区块链公开性的基础上增添了用户的密钥,通过用户的独有密钥使用户使用公共的星间通信网络进行通信,实现星间通信网络安全加密;实验结果表明,基于区块链技术的星间通信网络安全加密系统能够有效提高网络安全加密系统加密深度,降低误码率。