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.

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 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 SATELLITE IMAGING DIRECTION ANGLES ESTIMATION METHOD BASED ON RATIONAL POLYNOMIAL COEFFICIENTS

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 527-532
Author(s):  
H. Yi ◽  
X. Chen ◽  
D. Wang ◽  
S. Du ◽  
N. Guo ◽  
...  
Keyword(s):  
Satellite Images ◽  
Incidence Angle ◽  
Estimation Error ◽  
Estimation Method ◽  
Azimuth Angle ◽  
Camera Network ◽  
Angle Estimation ◽  
Satellite Imaging ◽  
Polynomial Coefficients ◽  
Rational Polynomial

Abstract. Satellite imaging direction angles, including the azimuth angle and the incidence angle, are the basic information used for satellite camera network structure analysis. They play an important role in 3D reconstruction using satellite images. In this paper, a satellite imaging direction angle estimation method based on rational polynomial coefficients is proposed for use when the satellite imaging direction angles are not available. Using rational polynomial coefficients, a vertical line on the ground is projected into the image plane, and the satellite imaging direction angles are estimated by analyzing the projection. Satellite images acquired by SPOT6, SOPT7 and Pleiades with different satellite imaging direction angles were used to test the feasibility of the proposed method. The experimental results were analyzed in detail combined with the method and the data. The experimental results show that the azimuth angle estimation error is less than 1.30 degrees, and the incidence angle estimation error is less than 0.83 degrees. This level of accuracy is sufficient for satellite camera network structure analysis.

scholarly journals Fixed-Point Arithmetic in FPGA

10.14311/692 ◽  
2005 ◽  
Vol 45 (2) ◽  
Author(s):  
M. Bečvář ◽  
P. Štukjunger
Keyword(s):  
Fixed Point ◽  
Integrated Circuits ◽  
Integrated Circuit ◽  
Digital Integrated Circuits ◽  
Arithmetic Operations ◽  
Gate Arrays ◽  
Fixed Point Arithmetic ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Point Arithmetic

Arithmetic operations are among the most frequently-used operations in contemporary digital integrated circuits. Various structures have been designed, utilizing different features of IC architectures. Nevertheless, there are very few studies that consider the design of arithmetic operations in Field Programmable Gate Arrays (FPGAs), a re-programmable type of digital integrated circuit. This text compares the results achieved when implementation of basic fixed-point arithmetic units in FPGA. 

scholarly journals KOMPSAT-3 Digital Elevation Model Correction Based on Point-to-Surface Matching

2019 ◽  
Vol 11 (20) ◽  
pp. 2340 ◽  
Author(s):  
Hyoseong Lee ◽  
Michael Hahn
Keyword(s):  
Satellite Images ◽  
Surface Matching ◽  
Control Points ◽  
Polynomial Coefficients ◽  
Ground Control Points ◽  
Rational Polynomial ◽  
Digital Elevation ◽  
High Resolution Satellite Images ◽  
Coordinate Measurements ◽  
Elevation Model

In order to generate digital elevation models (DEMs) from high-resolution satellite images, the vendor-provided rational polynomial coefficients (RPCs) are commonly used. This results in a level of accuracy that can be improved by using ground control points (GCPs). The integration of the GCPs into the processing chain is associated with additional effort, since it requires the provision of GCPs as well as the measurement of its image coordinates. In this paper, the authors avoid the measurement of GCP image coordinates and propose a point-to-surface matching method to correct the DEM produced from KOMPSAT-3 satellite images and the provided RPCs. For point-to-surface matching, an existing network of GCPs was used in South Korea, the so-called united control points and the triangulation control points. Practical testing was summarized with the proposed method in which the root mean square error with respect to the horizontal position and the height reduced from 20 m and 6 m to 3 m and 2 m, respectively. This demonstrates that neither image coordinate measurements nor additional GCP point acquisition, e.g., by GPS, are necessary to convert a DEM generated from KOMPSAT-3 images and vendor-provided RPCs into a highly accurate DEM by using existing GCPs and point-to-surface matching.

scholarly journals Numerical algorithms for high-performance computational science

2020 ◽  
Vol 378 (2166) ◽  
pp. 20190066 ◽  
Author(s):  
Jack Dongarra ◽  
Laura Grigori ◽  
Nicholas J. Higham
Keyword(s):  
High Performance ◽  
Numerical Algorithms ◽  
Computational Science ◽  
Floating Point ◽  
Important Criterion ◽  
Data Movement ◽  
Floating Point Arithmetic ◽  
High Performance Computers ◽  
Point Arithmetic ◽  
Speed And Accuracy

A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We discuss some approaches that we can take to develop numerical algorithms for high-performance computational science, with a view to exploiting the next generation of supercomputers. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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