Evaluation of the RPC Model for Ziyuan-3 Three-Line Array Imagery

2017 ◽  
Author(s):  
Zhonghua Hong ◽  
Shengyuan Xu ◽  
Yun Zhang ◽  
Yanling Han ◽  
Yongjiu Feng
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Generic Model ◽  
Axis Direction ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Line Array ◽  
Physical Sensor ◽  
Sensor Models

Ziyuan-3 (ZY-3) satellite is the first civilian stereo mapping satellite in China and was designed to achieve the 1: 50000 scale mapping for land and ocean. Rigorous sensor model (RSM) is required to build the relationship between the three-dimensional (3D) object space and two-dimensional (2D) image space of ZY-3 satellite imagery. However, each satellite sensor has its own imaging system with different physical sensor models, which increase the difficulty of geometric integration of multi-source images with different sensor models. Therefore, it is critical to generate generic model especially rational polynomial coefficients (RPCs) of optical imagery. Recently, relatively a few researches have been conducted on RPCs generation to ZY-3 satellite. This paper proposes an approach to evaluate the performance of RPCs generation from RSM of ZY-3 imagery. Three scenarios experiments with different terrain features (such as ocean, city and grassland) are designed and conducted to comprehensively evaluate the replacement accuracies of this approach and analyze the RPCs fitting error. All the experimental results demonstrate that the proposed method achieved encouraging accuracy of better than 1.946E-04 pixel in both x-axis direction and y-axis direction, it indicates that the RPCs is suitable for ZY-3 imagery and can be used as a replacement for the RSM of ZY-3 imagery.

scholarly journals Evaluation of the RPC Model for Ziyuan-3 Three-line Array Imagery

2018 ◽  
Author(s):  
Zhonghua Hong ◽  
Shengyuan Xu ◽  
Yun Zhang ◽  
Yanling Han ◽  
Yongjiu Feng
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Generic Model ◽  
Axis Direction ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Line Array ◽  
Physical Sensor ◽  
Sensor Models

Ziyuan-3 (ZY-3) satellite is the first civilian stereo mapping satellite in China and was designed to achieve the 1: 50000 scale mapping for land and ocean. Rigorous sensor model (RSM) is required to build the relationship between the three-dimensional (3D) object space and two-dimensional (2D) image space of ZY-3 satellite imagery. However, each satellite sensor has its own imaging system with different physical sensor models, which increase the difficulty of geometric integration of multi-source images with different sensor models. Therefore, it is critical to generate generic model， especially rational polynomial coefficients (RPCs) of optical imagery. Recently, relatively a few researches have been conducted on RPCs generation to ZY-3 satellite. This paper proposes an approach to evaluate the performance of RPCs generation from RSM of ZY-3 imagery. Three scenarios experiments with different terrain features (such as ocean, city and grassland) are designed and conducted to comprehensively evaluate the replacement accuracies of this approach and analyze the RPCs fitting error. All the experimental results demonstrate that the proposed method achieved the encouraging accuracy of better than 1.946E-04 pixel in both x-axis direction and y-axis direction, and it indicates that the RPCs is suitable for ZY-3 imagery and can be used as a replacement for the RSM of ZY-3 imagery.

scholarly journals Evaluation of the RPC Model for Ziyuan-3 Three-line Array Imagery

2017 ◽  
Author(s):  
Zhonghua Hong ◽  
Shengyuan Xu ◽  
Yun Zhang ◽  
Yanling Han ◽  
Yongjiu Feng
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Generic Model ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Line Array ◽  
Physical Sensor ◽  
Sensor Models ◽  
The Relationship

Ziyuan-3 (ZY-3) satellite is the first civilian stereo mapping satellite in China and was designed to achieve the 1:50000 scale mapping for land and ocean. Rigorous sensor model (RSM) is required to build the relationship between the three-dimensional (3D) object space and two-dimensional (2D) image space of ZY-3 satellite imagery. However, each satellite sensor has its own imaging system with different physical sensor models, which increase the difficulty of geometric integration of multi-source images with different sensor models. Therefore, it is critical to generate generic model especially rational polynomial coefficients (RPCs) of optical imagery. Recently, relatively a few researches have been conducted on RPCs generation to ZY-3 satellite. This paper proposes an approach to generate the RPCs for ZY-3 imagery from RSM. Three scenarios experiments with different terrain features (such as ocean, city and grassland) are designed and conducted to comprehensively evaluate the replacement accuracies of this approach and analyze the RPCs fitting error. All the experimental results demonstrate that the RPCs is suitable for ZY-3 imagery and can be used as a replacement for the RSM of ZY-3 imagery.

scholarly journals GEOPOSITIONING PRECISION ANALYSIS OF MULTIPLE IMAGE TRIANGULATION USING LRO NAC LUNAR IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 369-374
Author(s):  
K. Di ◽  
B. Xu ◽  
B. Liu ◽  
M. Jia ◽  
Z. Liu
Keyword(s):  
Three Dimensional ◽  
Landing Site ◽  
Vast Number ◽  
Multiple Image ◽  
Narrow Angle ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Lunar Reconnaissance Orbiter ◽  
Sensor Models ◽  
Image Pairs

This paper presents an empirical analysis of the geopositioning precision of multiple image triangulation using Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images at the Chang’e-3(CE-3) landing site. Nine LROC NAC images are selected for comparative analysis of geopositioning precision. Rigorous sensor models of the images are established based on collinearity equations with interior and exterior orientation elements retrieved from the corresponding SPICE kernels. Rational polynomial coefficients (RPCs) of each image are derived by least squares fitting using vast number of virtual control points generated according to rigorous sensor models. Experiments of different combinations of images are performed for comparisons. The results demonstrate that the plane coordinates can achieve a precision of 0.54 m to 2.54 m, with a height precision of 0.71 m to 8.16 m when only two images are used for three-dimensional triangulation. There is a general trend that the geopositioning precision, especially the height precision, is improved with the convergent angle of the two images increasing from several degrees to about 50°. However, the image matching precision should also be taken into consideration when choosing image pairs for triangulation. The precisions of using all the 9 images are 0.60 m, 0.50 m, 1.23 m in along-track, cross-track, and height directions, which are better than most combinations of two or more images. However, triangulation with selected fewer images could produce better precision than that using all the images.

scholarly journals ORTORECTIFICATION OF SPOT-4 DATA USING RATIONAL POLYNOMIAL COEFFICIENTS

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Danang Surya Candra
Keyword(s):  
Satellite Orbit ◽  
Parallel Projection ◽  
Mean Square ◽  
Approximation Model ◽  
Positioning Accuracy ◽  
Projection System ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Physical Sensor

Orthorectification  of  satellite  imagery  can  be  done  in  two  ways  i.e.,  rigorous sensor  model  and  the  approximation  model  of  the  satellite’s  orbit.  Dependence  on  physicalparameters,  to  make  rigorous  sensor  model  is  more  complicated  and  difficult  to  apply.  The approximation  model  can be either  Rational Polynomial Coefficients (RPC)  model  or  parallel projection  system.  RPC  is  a  mathematical  model  which  is  not  depends  on  the  sensor.  It  is used to improve the positioning accuracy when the parameter of the physical sensor model is  unknown.  This  study  assessed  orthorectification  of  SPOT-4  using  the  RPC  model  with  7 coefficients. Root Mean Square Error (RMSE) of GCPs obtained from the study  was less than 1  pixel.  RPC  did  not  depend  on  physical  and  satellite  orbit  parameters.  Thus  the  RPC  was simpler and easier to apply.

scholarly journals GEOPOSITIONING PRECISION ANALYSIS OF MULTIPLE IMAGE TRIANGULATION USING LRO NAC LUNAR IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 369-374 ◽  
Author(s):  
K. Di ◽  
B. Xu ◽  
B. Liu ◽  
M. Jia ◽  
Z. Liu
Keyword(s):  
Three Dimensional ◽  
Landing Site ◽  
Vast Number ◽  
Multiple Image ◽  
Narrow Angle ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Lunar Reconnaissance Orbiter ◽  
Sensor Models ◽  
Image Pairs

This paper presents an empirical analysis of the geopositioning precision of multiple image triangulation using Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images at the Chang’e-3(CE-3) landing site. Nine LROC NAC images are selected for comparative analysis of geopositioning precision. Rigorous sensor models of the images are established based on collinearity equations with interior and exterior orientation elements retrieved from the corresponding SPICE kernels. Rational polynomial coefficients (RPCs) of each image are derived by least squares fitting using vast number of virtual control points generated according to rigorous sensor models. Experiments of different combinations of images are performed for comparisons. The results demonstrate that the plane coordinates can achieve a precision of 0.54 m to 2.54 m, with a height precision of 0.71 m to 8.16 m when only two images are used for three-dimensional triangulation. There is a general trend that the geopositioning precision, especially the height precision, is improved with the convergent angle of the two images increasing from several degrees to about 50°. However, the image matching precision should also be taken into consideration when choosing image pairs for triangulation. The precisions of using all the 9 images are 0.60 m, 0.50 m, 1.23 m in along-track, cross-track, and height directions, which are better than most combinations of two or more images. However, triangulation with selected fewer images could produce better precision than that using all the images.

Satellite-Imagery Geometric Accuracy Improvement Based on Direct Correction of Dominant Coefficients

2020 ◽  
Vol 86 (4) ◽  
pp. 215-224
Author(s):  
Xinming Tang ◽  
Changru Liu ◽  
Ping Zhou ◽  
Ning Cao ◽  
FengXiang Li ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Geometric Accuracy ◽  
Accuracy Improvement ◽  
First Order ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Refinement Method ◽  
Correlation Problem

An important and difficult point in the application of satellite imagery is refining the positioning model and improving the geometric accuracy. In this study, we focus on improvement in geometric accuracy and develop a new rational function model (<small>RFM</small>) refinement method. First, we derive the conversion relationship between the rigorous sensor model and the <small>RFM</small>, based on which we illustrate the approximate meaning of the zero-order and first-order terms of the rational polynomial coefficients (<small>RPCs</small>). Second, the correlation problem between <small>RPCs</small> and the influence of individual <small>RPCs</small> on geometric positioning accuracy are analyzed and verified. The dominant coefficients that determine geolocation are then identified. Finally, a new <small>RFM</small> refinement method based on direct correction of the dominant coefficients is proposed and validated. The experiments, conducted with <small>ZY3-02</small> satellite imagery, indicate that the proposed method can effectively improve the geometric accuracy of satellite images.

scholarly journals QUASI-EPIPOLAR RESAMPLING OF HIGH RESOLUTION SATELLITE STEREO IMAGERY FOR SEMI GLOBAL MATCHING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 707-712 ◽  
Author(s):  
N. Tatar ◽  
M. Saadatseresht ◽  
H. Arefi ◽  
A. Hadavand
Keyword(s):  
Stereo Matching ◽  
Stereo Pair ◽  
Matching Algorithm ◽  
Automatic Feature Extraction ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Straight Line ◽  
Stereo Imagery ◽  
Global Matching

Semi-global matching is a well-known stereo matching algorithm in photogrammetric and computer vision society. Epipolar images are supposed as input of this algorithm. Epipolar geometry of linear array scanners is not a straight line as in case of frame camera. Traditional epipolar resampling algorithms demands for rational polynomial coefficients (RPCs), physical sensor model or ground control points. In this paper we propose a new solution for epipolar resampling method which works without the need for these information. In proposed method, automatic feature extraction algorithms are employed to generate corresponding features for registering stereo pairs. Also original images are divided into small tiles. In this way by omitting the need for extra information, the speed of matching algorithm increased and the need for high temporal memory decreased. Our experiments on GeoEye-1 stereo pair captured over Qom city in Iran demonstrates that the epipolar images are generated with sub-pixel accuracy.

scholarly journals SOLVING THE RATIONAL POLYNOMIAL COEFFICIENTS BASED ON L CURVE

2018 ◽  
Vol XLII-3 ◽  
pp. 2511-2515 ◽  
Author(s):  
G. Zhou ◽  
X. Li ◽  
T. Yue ◽  
W. Huang ◽  
C. He ◽  
...  
Keyword(s):  
Optimal Parameter ◽  
Least Square ◽  
Normal Equation ◽  
High Approximation ◽  
Camera Model ◽  
Approximation Accuracy ◽  
First Order ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial

The rational polynomial coefficients (RPC) model is a generalized sensor model, which can achieve high approximation accuracy. And it is widely used in the field of photogrammetry and remote sensing. Least square method is usually used to determine the optimal parameter solution of the rational function model. However the distribution of control points is not uniform or the model is over-parameterized, which leads to the singularity of the coefficient matrix of the normal equation. So the normal equation becomes ill conditioned equation. The obtained solutions are extremely unstable and even wrong. The Tikhonov regularization can effectively improve and solve the ill conditioned equation. In this paper, we calculate pathological equations by regularization method, and determine the regularization parameters by L&amp;thinsp;curve. The results of the experiments on aerial format photos show that the accuracy of the first-order RPC with the equal denominators has the highest accuracy. The high order RPC model is not necessary in the processing of dealing with frame images, as the RPC model and the projective model are almost the same. The result shows that the first-order RPC model is basically consistent with the strict sensor model of photogrammetry. Orthorectification results both the firstorder RPC model and Camera Model (ERDAS9.2 platform) are similar to each other, and the maximum residuals of X and Y are 0.8174&amp;thinsp;feet and 0.9272&amp;thinsp;feet respectively. This result shows that RPC model can be used in the aerial photographic compensation replacement sensor model.

scholarly journals Estimating intrafraction tumor motion during fiducial-based liver stereotactic radiotherapy via an iterative closest point (ICP) algorithm

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wu-zhou Li ◽  
Zhi-wen Liang ◽  
Yi Cao ◽  
Ting-ting Cao ◽  
Hong Quan ◽  
...  
Keyword(s):  
Stereotactic Radiotherapy ◽  
Imaging System ◽  
Liver Tumors ◽  
Three Dimensional ◽  
Rigid Motion ◽  
Iterative Closest Point ◽  
Tumor Motion ◽  
Tracking Accuracy ◽  
Icp Algorithm ◽  
Anterior Posterior

Abstract Background Tumor motion may compromise the accuracy of liver stereotactic radiotherapy. In order to carry out a precise planning, estimating liver tumor motion during radiotherapy has received a lot of attention. Previous approach may have difficult to deal with image data corrupted by noise. The iterative closest point (ICP) algorithm is widely used for estimating the rigid registration of three-dimensional point sets when these data were dense or corrupted. In the light of this, our study estimated the three-dimensional (3D) rigid motion of liver tumors during stereotactic liver radiotherapy using reconstructed 3D coordinates of fiducials based on the ICP algorithm. Methods Four hundred ninety-five pairs of orthogonal kilovoltage (KV) images from the CyberKnife stereo imaging system for 12 patients were used in this study. For each pair of images, the 3D coordinates of fiducial markers inside the liver were calculated via geometric derivations. The 3D coordinates were used to calculate the real-time translational and rotational motion of liver tumors around three axes via an ICP algorithm. The residual error was also investigated both with and without rotational correction. Results The translational shifts of liver tumors in left-right (LR), anterior-posterior (AP),and superior-inferior (SI) directions were 2.92 ± 1.98 mm, 5.54 ± 3.12 mm, and 16.22 ± 5.86 mm, respectively; the rotational angles in left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions were 3.95° ± 3.08°, 4.93° ± 2.90°, and 4.09° ± 1.99°, respectively. Rotational correction decreased 3D fiducial displacement from 1.19 ± 0.35 mm to 0.65 ± 0.24 mm (P<0.001). Conclusions The maximum translational movement occurred in the SI direction. Rotational correction decreased fiducial displacements and increased tumor tracking accuracy.

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