HJ-1A/B CCD IMAGERY Geometric Distortions and Precise Geometric Correction Accuracy Analysis

1. HJ-1A/B CCD IMAGERY Geometric Distortions and Precise Geometric Correction Accuracy Analysis Changmiao Hu, Ping Tang Email: akaishi@163.com, tangping@irsa.ac.cn Institute of Remote Sensing Applications Chinese Academy of Sciences Chao Yang, Beijing 100101

2. Content 1. Brief Introduction of HJ-1 A /B satellites 2. Geometric distortion analysis of HJ-1 CCD data 3. Comparison of different geometric correction models for HJ-1 CCD data 4. Conclusion

3. 1. Brief Introduction of HJ-1 A /B satellites • HJ-1 A and HJ-1 B satellites were launched on Sept. 5, 2008 by a Long March-2C in Taiyuan Satellite Launch Center, Shanxi Province, China. • HJ-1 A and HJ-1 B satellites together provide observation revisit cycle in 48 hours. The overall objective is to establish an operational earth observing system for environmental protection and disaster monitoring. Note: HJ is the abbreviation for Chinese pinyin “Huan Jing” - means “environment”. HJ-1 (Huan Jing-1: Environmental Protection & Disaster Monitoring Constellation)

4. HJ-1A is an optical satellite with two CCD cameras and an infrared camera; HJ-1B is also an optical satellite with two CCD cameras and a hyperspectral camera. The two pushbroom CCD cameras form a WVC ( Wide View CCD Cameras) Landsat TM HJ-1 A/B WVC

5. Technical parameters for multispectral CCD sensors of HJ and Landsat TM

6. 2. Geometric distortion analysis of HJ-1 CCD data • Test data Eight images from different satellites and CCD, which are after systematic geometric correction processing and have map projection information. The details of systematic geometric correction are unknown. • These images are from Satellite Environment Center (Ministry of Environmental Protection).

7. Eight images are displayed as a false-color composite(RGB-B432) after applying an identical linear stretch. No.1 No.2 No.3 No.4 No.5 No.6 No.7 No.8

8. Methods for distortion analysis • The automatic image matching method is adopted to obtain image control points, where HJ data as the original images, the Landsat TM GLCF images as reference images. • Drawing the displacement vectors of control points; • Calculate root mean squared error (RMSE) and analysis.

9. Image control points selection Over 1000 points 200 points 50 points About 1000 image control points evenly distributed are extracted by image matching in each test image. All the image control points are checked and the error matched points are deleted. Then 50, 200, and 1000 nearly even distributed control points are selected out, and some points are used as check points.

10. Displacement vectorsfor eight images, 50 points • There are both global system distortions of oriented shift and local distortions exist within the eight images. These distortions are quite different and not regular.

11. RMSE of eight images, geographical coordinates (meters), 50 points • The geometric precision of eight images are low. The total RMSE is from 500 to 1000 meters. • After systematic geometric correction processing, the HJ-1 A/B CCD images are still with low geometric precision and need to be geo-corrected in high precision.

12. 3. Comparison of different geometric correction models for HJ-1 CCD data • Three mathematical models are tested : • 1) Polynomial model (Global method) • 2) Thin plate splines (Global method with local characteristics) • 3) Finite element method (Local method)

13. Polynomial model • : the image coordinates. • : the ground coordinates. • : the coefficients, which always determined by least squares regression analysis. • Polynomial model is a global method. It always be used in small size image.

14. Polynomial model • RMSE in pixels. The control points are used for solving model. Both control points and check points are used for accuracy analysis. • The polynomial model is difficult to be used in correcting the eight images. The errors always larger than five pixels.

15. Thin plate splines (TPS) • : the image coordinates. • : the ground coordinates. • : the coefficients. • TPS is a global method, and also with local characteristics. It interpolates the control point rigorously, hence there are no residuals for the control points.

16. Thin plate splines (TPS) • RMSE in pixels, for check points. • When the number of control points is 40 or 180, the errors in check points are always larger than 5 pixels. • When the number of control points is over 1000, the calculation results of TPS are not stable.

17. Finite element method • Firstly construct Delaunay tessellation using the control points; • Then calculate the transformation parameters. In a Delaunay triangulation, use a 1st-order polynomial algorithm to do precise geometric correction. • Finally, interpolates the intensity of each pixel in the transformed file. • Finite element method is a local method, local variations do not directly affect the registration of the entire image. References: Jonathan Richard Shewchuk. “Triangle” A Two-Dimensional Quality Mesh Generator and Delaunay Triangulator. http://www.cs.cmu.edu/~quake/triangle.html

18. Finite element method • RMSE in pixels, for check points. • Finite element method can meet the requirement of precise geometric correction for HJ-1A/B CCD imagery if the number of evenly distributed control points are over 1000.

19. 4.Conclusion • Both global system distortions and complex local distortions exist within the HJ-1A/B CCD images; • Polynomial model gets the worst accuracy; • Thin plate splines significantly improve accuracy, but with the increase in the number of control points, the calculation is not stable. • Finite element method is recommended to be used of precise geometric correction for HJ-1A/B CCD imagery if the control points are enough and evenly distributed. Besides it is a local method, and possesses the advantages of rapidity and stability.

20. THE END THANK YOU Changmiao Hu, Ping Tang Email: akaishi@163.com, tangping@irsa.ac.cn