scholarly journals PROBLEMS AND LIMITATIONS OF SATELLITE IMAGE ORIENTATION FOR DETERMINATION OF HEIGHT MODELS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 257-264 ◽  
Author(s):  
K. Jacobsen
Keyword(s):  
Bias Correction ◽  
Satellite Image ◽  
Surface Model ◽  
Negative Effects ◽  
3 Dimensional ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Sensor Orientation ◽  
Image Orientation ◽  
Linear Trends

The usual satellite image orientation is based on bias corrected rational polynomial coefficients (RPC). The RPC are describing the direct sensor orientation of the satellite images. The locations of the projection centres today are without problems, but an accuracy limit is caused by the attitudes. Very high resolution satellites today are very agile, able to change the pointed area over 200km within 10 to 11 seconds. The corresponding fast attitude acceleration of the satellite may cause a jitter which cannot be expressed by the third order RPC, even if it is recorded by the gyros. Only a correction of the image geometry may help, but usually this will not be done. The first indication of jitter problems is shown by systematic errors of the y-parallaxes (py) for the intersection of corresponding points during the computation of ground coordinates. These y-parallaxes have a limited influence to the ground coordinates, but similar problems can be expected for the x-parallaxes, determining directly the object height. Systematic y-parallaxes are shown for Ziyuan-3 (ZY3), WorldView-2 (WV2), Pleiades, Cartosat-1, IKONOS and GeoEye. Some of them have clear jitter effects. In addition linear trends of py can be seen. Linear trends in py and tilts in of computed height models may be caused by limited accuracy of the attitude registration, but also by bias correction with affinity transformation. The bias correction is based on ground control points (GCPs). The accuracy of the GCPs usually does not cause some limitations but the identification of the GCPs in the images may be difficult. With 2-dimensional bias corrected RPC-orientation by affinity transformation tilts of the generated height models may be caused, but due to large affine image deformations some satellites, as Cartosat-1, have to be handled with bias correction by affinity transformation. Instead of a 2-dimensional RPC-orientation also a 3-dimensional orientation is possible, respecting the object height more as by 2-dimensional orientation. The 3-dimensional orientation showed advantages for orientation based on a limited number of GCPs, but in case of poor GCP distribution it may cause also negative effects. For some of the used satellites the bias correction by affinity transformation showed advantages, but for some other the bias correction by shift was leading to a better levelling of the generated height models, even if the root mean square (RMS) differences at the GCPs were larger as for bias correction by affinity transformation. <br><br> The generated height models can be analyzed and corrected with reference height models. For the used data sets accurate reference height models are available, but an analysis and correction with the free of charge available SRTM digital surface model (DSM) or ALOS World 3D (AW3D30) is also possible and leads to similar results. The comparison of the generated height models with the reference DSM shows some height undulations, but the major accuracy influence is caused by tilts of the height models. Some height model undulations reach up to 50&amp;thinsp;% of the ground sampling distance (GSD), this is not negligible but it cannot be seen not so much at the standard deviations of the height. In any case an improvement of the generated height models is possible with reference height models. If such corrections are applied it compensates possible negative effects of the type of bias correction or 2-dimensional orientations against 3-dimensional handling.

scholarly journals Geometric Accuracy Assessment of Deimos-2 Panchromatic Stereo Pairs: Sensor Orientation and Digital Surface Model Production

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7234
Author(s):  
Manuel A. Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando J. Aguilar
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Accuracy Assessment ◽  
Bare Soil ◽  
Surface Model ◽  
Stereo Pair ◽  
Rational Polynomial ◽  
Sensor Orientation ◽  
Vertical Accuracy ◽  
Land Covers

Accurate elevation data, which can be extracted from very high-resolution (VHR) satellite images, are vital for many engineering and land planning applications. In this way, the main goal of this work is to evaluate the capabilities of VHR Deimos-2 panchromatic stereo pairs to obtain digital surface models (DSM) over different land covers (bare soil, urban and agricultural greenhouse areas). As a step prior to extracting the DSM, different orientation models based on refined rational polynomial coefficients (RPC) and a variable number of very accurate ground control points (GCPs) were tested. The best sensor orientation model for Deimos-2 L1B satellite images was the RPC model refined by a first-order polynomial adjustment (RPC1) supported on 12 accurate and evenly spatially distributed GCPs. Regarding the Deimos-2 based DSM, its completeness and vertical accuracy were compared with those obtained from a WorldView-2 panchromatic stereo pair by using exactly the same methodology and semiglobal matching (SGM) algorithm. The Deimos-2 showed worse completeness values (about 6% worse) and vertical accuracy results (RMSEZ 42.4% worse) than those computed from WorldView-2 imagery over the three land covers tested, although only urban areas yielded statistically significant differences (p < 0.05).

scholarly journals Refining IKONOS DEM for Dehradun Region Using Photogrammetry Based DEM Editing Methods, Orthoimage Generation and Quality Assessment of Cartosat-1 DEM

2020 ◽  
Vol 5 (1) ◽  
pp. 3
Author(s):  
Ashutosh Bhardwaj ◽  
Kamal Jain ◽  
Rajat Subhra Chatterjee
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Digital Terrain Model ◽  
Optimal Number ◽  
Mean Difference ◽  
Surface Model ◽  
Difference Image ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
The Difference

The correct representation of the topography of terrain is an important requirement to generate photogrammetric products such as orthoimages and maps from high-resolution (HR) or very high-resolution (VHR) satellite datasets. The refining of the digital elevation model (DEM) for the generation of an orthoimage is a vital step with a direct effect on the final accuracy achieved in the orthoimages. The refined DEM has potential applications in various domains of earth sciences such as geomorphological analysis, flood inundation mapping, hydrological analysis, large-scale mapping in an urban environment, etc., impacting the resulting output accuracy. Manual editing is done in the presented study for the automatically generated DEM from IKONOS data consequent to the satellite triangulation with a root mean square error (RMSE) of 0.46, using the rational function model (RFM) and an optimal number of ground control points (GCPs). The RFM includes the rational polynomial coefficients (RPCs) to build the relation between image space and ground space. The automatically generated DEM initially represents the digital surface model (DSM), which is used to generate a digital terrain model (DTM) in this study for improving orthoimages for an area of approximately 100 km2. DSM frequently has errors due to mass points in hanging (floating) or digging, which need correction while generating DTM. The DTM assists in the removal of the geometric effects (errors) of ground relief present in the DEM (i.e., DSM here) while generating the orthoimages and thus improves the quality of orthoimages, especially in areas such as Dehradun that have highly undulating terrain with a large number of natural drainages. The difference image of reference, i.e., edited IKONOS DEM (now representing DTM) and automatically generated IKONOS DEM, i.e., DSM, has a mean difference of 1.421 m. The difference DEM (dDEM) for the reference IKONOS DEM and generated Cartosat-1 DEM at a 10 m posting interval (referred to as Carto10 DEM) results in a mean difference of 8.74 m.

scholarly journals PHOTOGRAMMETRIC PROCESSING USING ZY-3 SATELLITE IMAGERY

2015 ◽  
Vol XL-3/W2 ◽  
pp. 109-113 ◽  
Author(s):  
W. Kornus ◽  
A. Magariños ◽  
M. Pla ◽  
E. Soler ◽  
F. Perez
Keyword(s):  
High Resolution ◽  
Level Of Detail ◽  
Multispectral Image ◽  
Stereoscopic Vision ◽  
Surface Model ◽  
Pixel Size ◽  
Image Correction ◽  
North West ◽  
Polynomial Coefficients ◽  
Rational Polynomial

This paper evaluates the stereoscopic capacities of the Chinese sensor ZiYuan-3 (ZY-3) for the generation of photogrammetric products. The satellite was launched on January 9, 2012 and carries three high-resolution panchromatic cameras viewing in forward (22º), nadir (0º) and backward direction (-22º) and an infrared multi-spectral scanner (IRMSS), which is slightly looking forward (6º). The ground sampling distance (GSD) is 2.1m for the nadir image, 3.5m for the two oblique stereo images and 5.8m for the multispectral image. The evaluated ZY-3 imagery consists of a full set of threefold-stereo and a multi-spectral image covering an area of ca. 50km x 50km north-west of Barcelona, Spain. The complete photogrammetric processing chain was executed including image orientation, the generation of a digital surface model (DSM), radiometric image correction, pansharpening, orthoimage generation and digital stereo plotting. <br><br> All 4 images are oriented by estimating affine transformation parameters between observed and nominal RPC (rational polynomial coefficients) image positions of 17 ground control points (GCP) and a subsequent calculation of refined RPC. From 10 independent check points RMS errors of 2.2m, 2.0m and 2.7m in X, Y and H are obtained. Subsequently, a DSM of 5m grid spacing is generated fully automatically. A comparison with the Lidar data results in an overall DSM accuracy of approximately 3m. In moderate and flat terrain higher accuracies in the order of 2.5m and better are achieved. In a next step orthoimages from the high resolution nadir image and the multispectral image are generated using the refined RPC geometry and the DSM. After radiometric corrections a fused high resolution colour orthoimage with 2.1m pixel size is created using an adaptive HSL method. The pansharpen process is performed after the individual geocorrection due to the different viewing angles between the two images. In a detailed analysis of the colour orthoimage artifacts are detected covering an area of 4691ha, corresponding to less than 2% of the imaged area. Most of the artifacts are caused by clouds (4614ha). A minor part (77ha) is affected by colour patch, stripping or blooming effects. <br><br> For the final qualitative analysis on the usability of the ZY-3 imagery for stereo plotting purposes stereo combinations of the nadir and an oblique image are discarded, mainly due to the different pixel size, which produces difficulties in the stereoscopic vision and poor accuracy in positioning and measuring. With the two oblique images a level of detail equivalent to 1:25.000 scale is achieved for transport network, hydrography, vegetation and elements to model the terrain as break lines. For settlement, including buildings and other constructions a lower level of detail is achieved equivalent to 1:50.000 scale.

scholarly journals A DETAILED STUDY ABOUT DIGITAL SURFACE MODEL GENERATION USING HIGH RESOLUTION SATELLITE STEREO IMAGERY

2016 ◽  
Vol III-1 ◽  
pp. 69-76 ◽  
Author(s):  
K. Gong ◽  
D. Fritsch
Keyword(s):  
High Resolution ◽  
Stereo Matching ◽  
Research Area ◽  
Surface Model ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Dense Stereo Matching ◽  
Stereo Imagery ◽  
Satellite Sensors ◽  
High Resolution Satellite Imagery

Photogrammetry is currently in a process of renaissance, caused by the development of dense stereo matching algorithms to provide very dense Digital Surface Models (DSMs). Moreover, satellite sensors have improved to provide sub-meter or even better Ground Sampling Distances (GSD) in recent years. Therefore, the generation of DSM from spaceborne stereo imagery becomes a vivid research area. This paper presents a comprehensive study about the DSM generation of high resolution satellite data and proposes several methods to implement the approach. The bias-compensated Rational Polynomial Coefficients (RPCs) Bundle Block Adjustment is applied to image orientation and the rectification of stereo scenes is realized based on the Project-Trajectory-Based Epipolarity (PTE) Model. Very dense DSMs are generated from WorldView-2 satellite stereo imagery using the dense image matching module of the C/C++ library LibTsgm. We carry out various tests to evaluate the quality of generated DSMs regarding robustness and precision. The results have verified that the presented pipeline of DSM generation from high resolution satellite imagery is applicable, reliable and very promising.

scholarly journals POINT-TO-SURFACE MATCHING FOR DEM CORRECTION USING ICESAT DATA

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 715-720
Author(s):  
H. Lee ◽  
M. Hahn
Keyword(s):  
Satellite Images ◽  
Experimental Investigations ◽  
Surface Matching ◽  
Satellite Mission ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Sensor Orientation ◽  
Digital Elevation ◽  
High Resolution Satellite Images ◽  
Starting Point

Abstract. Vendor-provided rational polynomial coefficients (RPCs) are commonly used to generate digital elevation models (DEMs) from high-resolution satellite images. This results in a level of accuracy that can be improved using ground control points (GCPs). It is well known that due to the inherent bias of sensor orientation the generated DEM is distorted. In the traditional way of working, the bias is corrected by integrating GCPs into the standard processing chain. This involves additional effort, since the provision of GCPs and the measurement of their image coordinates are required.In this paper, we examine whether and how the data recorded by NASA's ICESat (Ice, Cloud, and Land Elevation Satellite) mission can be used as GCPs without measuring image coordinates. The starting point are DEMs that are generated by image matching from KOMPSAT-3 satellite images with given RPCs. We developed a point-to-surface matching method that matches the ICESat points to the DEM in order to correct the DEM and improve its precision. For the experimental investigations a KOMPSAT 3 image pair is used that covers an area of 20 by 16 km2 in the Yangsan city regions. The generated DEM has a height accuracy of about 9 m. The point-to-surface algorithm with 505 ICESat points led to an improvement of the DEM height accuracy to about 2 m.

scholarly journals DEM GENERATION WITH SHORT BASE LENGTH PLEIADES TRIPLET

2015 ◽  
Vol XL-3/W2 ◽  
pp. 81-86 ◽  
Author(s):  
K. Jacobsen ◽  
H. Topan
Keyword(s):  
Small Angle ◽  
Stereo Pair ◽  
Space Images ◽  
Polynomial Coefficients ◽  
Ground Control Points ◽  
Rational Polynomial ◽  
Vertical Accuracy ◽  
Intersection Geometry ◽  
Image Orientation ◽  
Height Model

An image triplet of Pleiades images covering the area of Zonguldak, Turkey has been investigated. The height to base relation of the first to the last image is just 1:4.5 and for the first and the second image 1:9. This is quite below the usual height to base relation of 1:1.6 for a typical stereo pair of space images. The corresponding small angle of convergence influences the possible vertical accuracy, but images with such a small angle of convergence are more similar to each other as images with larger convergence angles. This enables a better image matching, improving the vertical accuracy and compensating partially the influence of poor intersection geometry. Even over forest areas no matching gaps occurred. Height models are generated with different base configurations and compared with a reference height model. Pleiades images are distributed with 50cm ground sampling distance instead of the physical size of 70cm, the image quality justifies this zooming and also the geometric results are in the range of other space images with originally 50cm GSD. The image orientation by bias corrected Rational Polynomial Coefficients (RPC) is leading with more as 160 ground control points (GCP) to root mean square (RMS) differences slightly below 1.0 GSD of the distributed images (0.5m GSD). Only negligible systematic errors have been identified. With the combination of the first and last image a standard deviation of the generated height model of 1.6m, respectively for flat terrain close to 1.0m has been reached in relation to a reference height model. The small angle of convergence is not as much influencing the height accuracy as according to simple geometric relation.

scholarly journals Positioning Accuracy Analysis and Application for Worldview-1 Stereo Imagery

2014 ◽  
Vol XL-4 ◽  
pp. 311-314
Author(s):  
Z. Xiao ◽  
B. Yang ◽  
H. Zhang
Keyword(s):  
Satellite Image ◽  
Systematic Errors ◽  
Control Points ◽  
Accuracy Analysis ◽  
Positioning Accuracy ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Stereo Imagery

This article introduced the progress of processing the WorldView-1 satellite image by using the air triangulation method．And different adjustment models were used to improve the vendor provided RPC (Rational Polynomial Coefficients) accuracy. WorldVfew-1 images in Beijing are used to test the correction accuracy of these adjustment models．Results show that the systematic errors of RPC model can be eliminated using a small amount of control points. The planar RMSE can reach 1.6 pixels (0.9 meter)．

scholarly journals RELATIVE ORIENTATION AND MODIFIED PIECEWISE EPIPOLAR RESAMPLING FOR HIGH RESOLUTION SATELLITE IMAGES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 579-586 ◽  
Author(s):  
K. Gong ◽  
D. Fritsch
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Relative Orientation ◽  
Ground Control ◽  
Surface Model ◽  
Control Information ◽  
Rational Polynomial ◽  
High Resolution Satellite Images ◽  
Resampling Method

High resolution, optical satellite sensors are boosted to a new era in the last few years, because satellite stereo images at half meter or even 30cm resolution are available. Nowadays, high resolution satellite image data have been commonly used for Digital Surface Model (DSM) generation and 3D reconstruction. It is common that the Rational Polynomial Coefficients (RPCs) provided by the vendors have rough precision and there is no ground control information available to refine the RPCs. Therefore, we present two relative orientation methods by using corresponding image points only: the first method will use quasi ground control information, which is generated from the corresponding points and rough RPCs, for the bias-compensation model; the second method will estimate the relative pointing errors on the matching image and remove this error by an affine model. Both methods do not need ground control information and are applied for the entire image. To get very dense point clouds, the Semi-Global Matching (SGM) method is an efficient tool. However, before accomplishing the matching process the epipolar constraints are required. In most conditions, satellite images have very large dimensions, contrary to the epipolar geometry generation and image resampling, which is usually carried out in small tiles. This paper also presents a modified piecewise epipolar resampling method for the entire image without tiling. The quality of the proposed relative orientation and epipolar resampling method are evaluated, and finally sub-pixel accuracy has been achieved in our work.

scholarly journals A NOVEL RPC BIAS MODEL FOR IMPROVING THE POSITIONING ACCURACY OF SATELLITE IMAGES

2020 ◽  
Vol V-2-2020 ◽  
pp. 35-41
Author(s):  
Y. Dong ◽  
R. Lei ◽  
D. Fan ◽  
L. Gu ◽  
S. Ji
Keyword(s):  
Satellite Image ◽  
Image Data ◽  
Positioning Accuracy ◽  
Polynomial Coefficients ◽  
Positioning Errors ◽  
Rational Polynomial ◽  
Bias Model ◽  
Actual Error ◽  
Satellite Image Data ◽  
Correct Image

Abstract. High-precision satellite image geolocation is the basis for advanced processing of satellite image data. Aiming at the optimization of the satellite image positioning accuracy based on rational polynomial coefficients (RPC), we propose an RPC image-space bias model that combines object-space information. Based on a comprehensive analysis of the full-link error of the satellite image geometric imaging process, the real object coordinates are introduced into the RPC correction to make the bias model better fit the actual error. Experiments were performed using several image datasets from the Chinese satellite TianHui-1 (TH-1) and compared with the traditional RPC bias model. The results show that our model has strong robustness and can better correct image positioning errors. Compared with traditional bias models, it can improve the accuracy of plane positioning by approximately 1 pixel.

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