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 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.

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 Geometric Accuracy Study of Orthorectification Based on Sensor Model Refinement in Imagery Subset Using ORFEO Toolbox (OTB)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Andri Suprayogi ◽  
Nurhadi Bashit
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Ground Control ◽  
Surface Model ◽  
Control Points ◽  
Geometric Accuracy ◽  
Model Refinement ◽  
Full Extent ◽  
Sensor Model ◽  
Ground Control Points

Large scale base map can be obtained by various methods, one of them is orthorectification process of remote sensing satellite imagery to eliminate the relief displacement caused by height variation of earth surface. To obtain a  map images with good quality,  it requires additional data such as sensor model in the form of rational polynomial coefficients (RPC), surface model data, and ground control points Satellite imageries with high resolution  file size are relatively large.  In order to process them,  high specification of hardwares were required. To overcome this by cutting only a portion of the images, based on certain study areas were suffer from of georeference lost so it would not be able to orthorectified. On the other hand,  in several remote sensing software such as ESA SNAP and Orfeo Toolbox (OTB)  subset or pixel extraction from satellite imagery,  preserve the imagery geometric sensor models. This research aimed at geometric accuracy of orthorectification carried out in a single scene of Pleiades Imagery within the Kepahiang Subdistrict, located at Kepahiang Regency, Bengkulu Province, by using DEMNAS and the imagery refined sensor mode, and ground control points taken using GPS Survey. Related with the raw imagery condition which consists of Panchromatic and multispectral bands, this study were separated to assembly, pan sharpening , and sensor model refinement stages prior to orthorectification carried out both in the original or full extent imagery and the result of subset extent imagery. After  these processses taken place, we measure the accuracy of each full and subset imagery.These procedures were carried out using Orfeo toolbox 6.6.0 in the Linux Mint 19 Operating system. From the process log, running time in total  were 7814.518  second for the full extent and 4321.95 seconds for the subset processess. And as a big data process, the total of full extent imageries was 83.15 GB  while the subset size  was  only 30.73 GB.  The relative accuracy of the full extent and its subset imagery were 0.431 meters. Accuracy of the  sensor model refinement process are  1.217 meters and 1.550 meters with GCP added, while the accuracu of  the orthorectifications results were  0.416 meters and 0.751 meters by using ICP.  Variation of execution time may caused by the data input size and complexity of the mathematical process carried out in each stages. Meanwhile,  the variation of accuracy may  caused by the check or control points placements above satellite Imagery which suffer from uncertainty when dealing with  the sub-pixel position or under 0.5 meters.

scholarly journals RPC-Based Orthorectification for Satellite Images Using FPGA

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2511 ◽  
Author(s):  
Rongting Zhang ◽  
Guoqing Zhou ◽  
Guangyun Zhang ◽  
Xiang Zhou ◽  
Jingjin Huang
Keyword(s):  
Fixed Point ◽  
Processing Speed ◽  
Satellite Images ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Field Programmable ◽  
Fpga Chip ◽  
Mean Square Errors ◽  
Point Arithmetic ◽  
Speed And Accuracy

Conventional rational polynomial coefficients (RPC)-based orthorectification methods are unable to satisfy the demands of timely responses to terrorist attacks and disaster rescue. To accelerate the orthorectification processing speed, we propose an on-board orthorectification method, i.e., a field-programmable gate array (FPGA)-based fixed-point (FP)-RPC orthorectification method. The proposed RPC algorithm is first modified using fixed-point arithmetic. Then, the FP-RPC algorithm is implemented using an FPGA chip. The proposed method is divided into three main modules: a reading parameters module, a coordinate transformation module, and an interpolation module. Two datasets are applied to validate the processing speed and accuracy that are achievable. Compared to the RPC method implemented using Matlab on a personal computer, the throughputs from the proposed method and the Matlab-based RPC method are 675.67 Mpixels/s and 61,070.24 pixels/s, respectively. This means that the proposed method is approximately 11,000 times faster than the Matlab-based RPC method to process the same satellite images. Moreover, the root-mean-square errors (RMSEs) of the row coordinate (ΔI), column coordinate (ΔJ), and the distance ΔS are 0.35 pixels, 0.30 pixels, and 0.46 pixels, respectively, for the first study area; and, for the second study area, they are 0.27 pixels, 0.36 pixels, and 0.44 pixels, respectively, which satisfies the correction accuracy requirements in practice.

scholarly journals Optimizing Orientation by GCP Refinement of Very High Resolution IKONOS Satellite Images

2010 ◽  
pp. 35-39
Author(s):  
Madhusudan Adhikari
Keyword(s):  
Spatial Data ◽  
Satellite Images ◽  
Control Point ◽  
Large Error ◽  
Stereo Image ◽  
Ground Control ◽  
Polynomial Coefficients ◽  
Rational Polynomial ◽  
Transformation Algorithm ◽  
Very High

The Rational Polynomial Coefficients (RPC) provided with the IKONOS images contains a large error and they need Ground Control Point (GCP) refinement. To present the technique of refinement of RPCs by the application of some appropriate transformation algorithm with some suitable number of GCPs in proper constellation in an optimal way to achieve high geometric accuracy during spatial data acquisition from IKONOS stereo image is the objective of this paper. From this study it was found that GCP refinement of RPCs by affine transformation with four GCPs in proper constellation is optimal for the orientation of the image pair under study, it was also found that at least two redundant GCPs are necessary for proper refinement by a particular transformation algorithm.

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 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 GEOMETRIC ACCURACY EVALUATION OF THE ZY-3 STEREO MAPPING SATELLITE FOR 8 YEARS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 415-421
Author(s):  
L. Zhao
Keyword(s):  
Control Point ◽  
Stable Condition ◽  
Accuracy Evaluation ◽  
Geometric Accuracy ◽  
Polynomial Coefficients ◽  
Bundle Block Adjustment ◽  
Rational Polynomial ◽  
Affine Correction ◽  
Geolocation Accuracy ◽  
Better Than

Abstract. The Ziyuan-3 (ZY-3) is the first civil high-resolution optical stereoscopic mapping satellite developed by China. It was launched on January 9, 2012, and as of January 9, 2020, it has been in a continuous operation for 8 years in orbit. The direct geolocation performances of ZY-3 based on the Rational Polynomial Coefficients (RPC) model are discussed, and the monoscopic and stereoscopic geometric accuracy of ZY-3 with ground control point (GCP) have also been studied using a bundle block adjustment with the affine correction in image space. Through the analysis of 47 sets of ZY-3 stereo data in Harbin, China, it can be found that from 2012 to 2019, the ZY-3 geolocation accuracy without GCP remains stable, with an average planimetric accuracy of approximately 16.2m, an average elevation accuracy of about 7.5m. The internal accuracy of the panchromatic triplet images is better than 1.0 pixel, the planimetric accuracy and elevation accuracy of stereo triplets and and stereo pairs are better than 2.0 meters with control points. In general, the results show that the ZY-3 satellite has been in good and stable condition during in-orbit operation in the past 8 years, and the various test indicators have been able to meet the design specifications.

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.

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