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.

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

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 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.

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 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 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.

scholarly journals BeiDou Satellite Unhealthy States and the Impact on System Performance

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4196 ◽  
Author(s):  
Caibo Hu ◽  
Chuang Shi ◽  
Jinping Chen ◽  
Yidong Lou ◽  
Fei Wang
Keyword(s):  
System Performance ◽  
Satellite Orbit ◽  
Service Area ◽  
The Other ◽  
Classification Method ◽  
Positioning Accuracy ◽  
General Classification ◽  
Broadcast Ephemeris ◽  
Beidou System ◽  
The Impact

The BeiDou system satellites may be unhealthy due to many reasons, affecting system performance in different ways. Therefore, it is important to analyze the causes and characteristics of the satellites’ unhealthy states. In this study, these states are classified into five types based on the broadcast ephemeris. Three criteria are presented, based on which a general classification method is proposed. Data from July 2017 to June 2018 are analyzed to validate the method, from which we know that the average unhealthy duration due to satellite maneuvers is much longer than the duration of unhealthy states related to satellite orbit or clock anomalies, and the other unhealthy states may be caused by inbound or outbound satellites. Statistics show that most of the time, the number of unhealthy satellites is no more than two and the average positioning accuracy in the service area will decrease by no more than 0.75 and 1.2 meters when one or two BDS satellites are unhealthy, respectively.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

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