Three-Dimensional Geopositioning Accuracy of Ikonos Imagery

An investigation of the accuracy potential of Ikonos 1m satellite imagery is reported. Three sensor orientation/triangulation models are applied to stereo- and three-image configurations of "Geo" imagery with the aim of achieving 3D geopositioning to sub-metre accuracy. The models considered comprise rational functions with bias compensation, affine projection and the direct linear transformation. Test results from the Melbourne Ikonos Testfield are reported and these show that with modest provision of good quality ground control, Ikonos "Geo" imagery can yield 3D object-point determination to an accuracy of 0.5m in planimetry and 0.7m in height. The accuracy achieved is not only consistent with expectations for rigorous sensor orientation models, but is also readily attainable in practice with only a small number of ground control points being required     

Mapping spiny aster infestations with QuickBird imagery

QuickBird satellite imagery acquired in June 2003 and September 2004 was evaluated for detecting the noxious weed spiny aster [Leucosyris spinosa (Benth.) Greene] on a south Texas, USA rangeland area. A subset of each of the satellite images representing a diversity of cover types was extracted and used as a study site. The satellite imagery had a spatial resolution of 2.8 m and contained 11-bit data. Unsupervised and supervised classification techniques were used to classify false colour composite (green, red, and near-infrared bands) images of the study site. Imagery acquired in June was superior to that obtained in September for distinguishing spiny aster infestations. This was attributed to differences in spiny aster phenology between the two dates. An unsupervised classification of the June image showed that spiny aster had producer's and user's accuracies of 90% and 93.1%, respectively, whereas a supervised classification of the June image had producer's and user's accuracies of 90% and 81.8%, respectively. These results indicate that high resolution satellite imagery coupled with image analysis techniques can be used successfully for detecting spiny aster infestations on rangelands.     

QuickBird影像在土地调查中的精度评价

利用遥感的手段对土地利用现状、土地基础数据进行调查。调查以0．61m分辨率的QuickBird影像为数据源,着重从混合像元成像机制、误差产生规律等方面量化0．61m分辨率的QuickBird影像的评价精度,并以各种地物类型的面积、边界线等因子对分类结果进行精度评价。     

Using high resolution QuickBird imagery for crop identification and area estimation

Imagery from recently launched high spatial resolution satellite sensors offers new opportunities for crop assessment and monitoring. A 2.8-m multispectral QuickBird image covering an intensively cropped area in south Texas was evaluated for crop identification and area estimation. Three reduced-resolution images with pixel sizes of 11.2 m, 19.6 m, and 30.8 m were also generated from the original image to simulate coarser resolution imagery from other satellite systems. Supervised classification techniques were used to classify the original image and the three aggregated images into five crop classes (grain sorghum, cotton, citrus, sugarcane, and melons) and five non-crop cover types (mixed herbaceous species, mixed brush, water bodies, wet areas, and dry soil/roads). The five non-crop classes in the 10-category classification maps were then merged as one class. The classification maps were filtered to remove the small inclusions of other classes within the dominant class. For accuracy assessment of the classification maps, crop fields were ground verified and field boundaries were digitized from the original image to determine reference field areas for the five crops. Overall accuracy for the unfiltered 2.8-m, 11.2-m, 19.6-m, and 30.8-m classification maps were 71.4, 76.9, 77.1, and 78.0%, respectively, while overall accuracy for the respective filtered classification maps were 83.6, 82.3, 79.8, and 78.5%. Although increase in pixel size improved overall accuracy for the unfiltered classification maps, the filtered 2.8-m classification map provided the best overall accuracy. Percentage area estimates based on the filtered 2.8-m classification map (34.3, 16.4, 2.3, 2.2, 8.0, and 36.8% for grain sorghum, cotton, citrus, sugarcane, melons, and non-crop, respectively) agreed well with estimates from the digitized polygon map (35.0, 17.9, 2.4, 2.1, 8.0, and 34.6% for the respective categories). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas.     

Insights into the affine model for high-resolution satellite sensor orientation

As a model for sensor orientation and 3D geopositioning for high-resolution satellite imagery (HRSI), the affine transformation from object to image space has obvious advantages. Chief among these is that it is a straightforward linear model, comprising only eight parameters, which has been shown to yield sub-pixel geopositioning accuracy when applied to Ikonos stereo imagery. This paper aims to provide further insight into the affine model in order to understand why it performs as well as it does. Initially, the model is compared to counterpart, ‘rigorous’ affine transformation formulations which account for the conversion from a central perspective to affine image. Examination of these rigorous models sheds light on issues such as the effects of terrain and size of area, as well as upon the choice of reference coordinate system and the impact of the adopted scanning mode of the sensor. The results of application of the affine sensor orientation model to four multi-image Ikonos test field configurations are then presented. These illustrate the very high geopositioning accuracy attainable with the affine model, and illustrate that the model is not affected by size of area, but can be influenced to a modest extent by mountainous terrain, the mode of scanning and the choice of object space coordinate system. Above all, the affine model is shown to be both a robust and practical sensor orientation/triangulation model with high metric potential.     

An improved geopositioning model of QuickBird high resolution satellite imagery by compensating spatial correlated errors

A lot of studies have been done for correcting the systematic biases of high resolution satellite images (HRSI), which is a fundamental work in the geometric orientation and the geopositioning of HRSI. All the existing bias-corrected models eliminate the biases in the images by expressing the biases as a function of some deterministic parameters (i.e. shift, drift, or affine transformation models), which is indeed effective for most of the commercial high resolution satellite imagery (i.e. IKONOS, GeoEye-1, WorldView-1/2) except for QuickBird. Studies found that QuickBird is the only one that needs more than a simple shift model to absorb the strong residual systematic errors. To further improve the image geopositioning of QuickBird image, in this paper, we introduce space correlated errors (SCEs) and model them as signals in the bias-corrected rational function model (RFM) and estimate the SCEs at the ground control points (GCPs) together with the bias-corrected parameters using least squares collocation. With these estimated SCEs at GCPs, we then predict the SCEs at the unknown points according to their stochastic correlation with SCEs at the GCPs. Finally, we carry out geopositioning for these unknown points after compensating both the biases and the SCEs. The performance of our improved geopositioning model is demonstrated with a stereo pair of QuickBird cross-track images in the Shanghai urban area. The results show that the SCEs exist in HRSI and the presented geopositioning model exhibits a significant improvement, larger than 20% in both latitude and height directions and about 2.8% in longitude direction, in geopositioning accuracy compared to the common used affine transformation model (ATM), which is not taking SCEs into account. The statistical results also show that our improved geopositioning model is superior to the ATM and the second polynomial model (SPM) in both accuracy and reliability for the geopositioning of HRSI.     

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.     

几何纠正模式对QuickBird全色影像定位精度的影响——以黄土高原为例

以陕北黄土高原的QuickBird全色影像为例,设计了4种几何精纠正模式并试验其对纠正后影像平面精度的影响.结果表明:①在控制点(GCP)均匀分布的条件下,随着GCP个数的不同,GCP残差的变幅远大于纠正后影像均方根的变幅;②利用实测GPS点一正射纠正模式纠正后的影像精度最高,1∶10 000地形图一正射纠正模式次之,...     

Effect of sensor modelling methods on computation of 3-D coordinates from Cartosat-1 stereo data

The orbital and the rational polynomial coefficients (RPC) models are the two most commonly used models to compute a three-dimensional coordinates from an image stereo-pair. But it is still confusing that with the identical user provided inputs, which one of these two models provides more accurate digital elevation model (DEM), especially for mountainous terrain. This study aimed to find out the answer by evaluating the impact of used models on the vertical accuracy of DEM extracted from Cartosat-1 stereo data. We used high-accuracy photogrammetric DEM as the reference DEM. Apart from general variations in statistics, surprisingly in a few instances, both the DEMs provided contrasting results, thus proving the significance of this study. The computed root mean square errors and linear error at 90% (LE90) were lower in case of RPC DEM for various classes of slope, aspect and land cover, thus suggesting its better relative accuracy.     

“天绘一号”卫星三线阵影像条带式区域网平差

针对框幅式影像传统的空中三角测量算法不适用于三线阵影像, 因此必须设计相应的数学模型。三线阵推扫式传感器在成像时, 不同扫描线对应的摄影中心位置和姿态都不一样, 空中三角测量解算时, 外方位元素个数大于观测值个数, 理论上无法解算每一条扫描线的方位元素, 因此需要采用合适的数学模型模拟卫星轨道。目前常用的有3种轨道模型:线性多项式模型, 分段多项式模型和定向片模型。本文利用“天绘一号”卫星的真实数据, 在WGS-84坐标系统下进行3种模型的平差对比试验, 同时采用不同的控制点布设方案, 分析各模型在不同控制点布设方案下所能达到的精度水平。     

Estimating vegetation parameter for soil erosion assessment in an alpine catchment by means of QuickBird imagery

Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km²). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r² = 0.64, r² = 0.71, respectively). Best results were achieved for LSU (r² = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r² = 0.39 for LSU, r² = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.     

基于有理函数模型的多源SAR遥感影像区域网平差

本文针对多源SAR遥感影像联合定位的实际问题,将有理函数模型引入到SAR影像联合定位中,构建了基于有理函数模型的多源SAR区域网平差模型.对覆盖我国某地区的3景不同源SAR影像进行试验,验证了本文方法的有效性,并表明在缺少地面控制情况下,该方法不失为一种有益的补充方案.     

Quality Assessment Of Digital Surface Models Generated From IKONOS Imagery

The growing applications of digital surface models (DSMs) for object detection, segmentation and representation of terrestrial landscapes have provided impetus for further automation of 3D spatial information extraction processes. While new technologies such as lidar are available for almost instant DSM generation, the use of stereoscopic high-resolution satellite imagery (HRSI), coupled with image matching, affords cost-effective measurement of surface topography over large coverage areas. This investigation explores the potential of IKONOS Geo stereo imagery for producing DSMs using an alternative sensor orientation model, namely bias-corrected rational polynomial coefficients (RPCs), and a hybrid image-matching algorithm. To serve both as a reference surface and a basis for comparison, a lidar DSM was employed in the Hobart testfield, a region of differing terrain types and slope. In order to take topographic variation within the modelled surface into account, the lidar strip was divided into separate sub-areas representing differing land cover types. It is shown that over topographically diverse areas, heighting accuracy to better than 3 pixels can be readily achieved. Results improve markedly in feature-rich open and relatively flat terrain, with sub-pixel accuracy being achieved at check points surveyed using the global positioning system (GPS). This assessment demonstrates that the outlook for DSM generation from HRSI is very promising.     

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.     

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 ,…     

QuickBird影像在城市建设中的应用研究

对0.61 m地面分辨率QuickBird影像的几何精度进行初步评价,并对QuickBird影像在城市建设中的应用进行分析,针对其应用的内容、方案及效果进行了总结和探讨.分析表明,QuickBird影像能为城市规划管理以及土地利用管理等提供众多基础空间数据,具有广阔的应用前景.     

Accuracy assessment of lidar-derived digital elevation models

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error data-sets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.     

提高无控制点星载SAR影像的定位精度研究

针对无控制点情况下星载SAR地面目标高精度定位问题,提出在整个成像条带中任选一景SAR影像,并基于全球DEM数据生成模拟的SAR影像,然后通过自动或半自动匹配方式寻找一个控制点,修正整个条带的轨道参数,从而消除了系统误差的影响,提高了整个SAR条带影像的定位精度。     

面向森林高度提取的光学多角度立体观测影像模拟

Canopy height is one of important indicators of carbon storage in forest ecosystems. Previous studies have demonstrated that optical stereo imagery held great potential for deriving forest canopy height. However, optical images are easily affected by cloud coverage and rains. The regional mapping of forest canopy height has to be achieved by the synthesis of multi-sensor and multi-temporal imagery. The spatial resolutions and viewing angles of existing spaceborne stereoscopic systems are quite different. It is essential to make a systematic investigation about the impact of image spatial resolutions and viewing angles on the vertical distribution of stereoscopic point clouds, which is the basis for the synergy of images acquired at different seasons even by different cameras. The theoretical model for the simulation of optical stereo imagery over forested areas is necessary for such studies. The LandStereo is such kind of model, which can simulate the along-track(only In-track viewing) stereoscopic imagery like ALOS/PRISM and ZY-3. However, the current version of LandStereo has no mode to simulate the along-track(including In-track and Cross-track) stereoscopic imagery like Worldview-1/2/3. Therefore, this study reported the modification of the LandStereo model to have it being able to simulate images acquired by any possible viewing directions in forest areas. Firstly, the calculation method of linear array projection center coordinates is improved, from originally only considering the change of observation altitude angle to considering the change of azimuth angle and altitude angle. Secondly, Rigorous imaging geometric model is improved to obtain RPC of images acquired by any possible viewing directions. Based on the improved LandStereo model, the bare and mountainous forest images with altitude angle of 75° and azimuth angle of 0°, 90°, and 225° are simulated to verify the accuracy and extract forest canopy height. The surface elevation extracted by the improved LandStereo model is consistent with the input DTM with high accuracy (r=0.99, RMSE=1.507), which proves the geometric accuracy of the improved LandStereo model. There are certain differences in the extraction accuracy of forest canopy height between stereo images of different angles. The results showed that the modified model could correctly simulate the stereoscopic features of forest canopy with given view direction, also initially demonstrated that view angle was an important factor affecting the estimation accuracy of forest canopy height by stereoscopic images. © 2023 Science Press. All rights reserved.