1. 2011 International Geoscience & Remote Sensing Symposium
Geo-location error correction for Synthetic Aperture Radar image Using the ground control point
29. July, 2011
Soo H. Rho, Jung Kim and Young. K Kwag
Radar Signal Processing Lab.
Department of Avionics, Korea Aerospace University, Seoul, Korea

2. To be Presented Introduction Spaceborne SAR Geometry
SAR Geo-location Error
Multiple Target Distortion
Overview Simulation
Proposed Algorithm
Simulation Results
Conclusions

3. Introduction SAR Geometric Calibration Necessity Geometric Correction
Processing step where the image is re-sampled from its natural distorted projection into an actual image in a real coordinate system
 Geocoding for standard map projection such as UTM, WGS
Geolocation
Accurate target location decided by SAR sensor parameter and the Earth model
Geometric Calibration
Process of measuring the various error sources such as sensor payload platform ephemeris errors, relative target height error, and SAR signal processing errors.
Necessity
In the real operating environment, the SAR estimation errors always exist
Accuracy of slant range measurement, Doppler centroid estimation performance, SAR operation characteristics (Side-looking) are to be analyzed and corrected in the process of the SAR image utilization.

4. Spaceborne SAR Geometry
Azimuth:
Direction aligned with the net sensor motion
Zero Doppler Plane:
Plane containing the sensor and is perpendicular to the platform velocity vector
Range of closest approach R2:
Range when zero Doppler line crosses the target
Position of closest approach P2 :
Closest position of sensor to the target
Zero Doppler time:
Time of the closest approach
[Geometry of Side-looking SAR]
[SAR Image Formation Process]

5. Comparison of E/O & SAR Image
[EO Image]
[SAR Image]

6. SAR Geo-location Error
Earth
Sensor
Platform
Skewed Image
Range Non-Linearity
Target Height
PRF Fluctuation
Electronic Time Delay
Incidence Angle
Doppler Centroid
Image Orientation
Yaw Angle
Pitch Angle

7. Geo-Location Error/Correction Method
Error Source
Effect of Error
Geo-location Error Correction Method
SAR sensor
- Electronic Time Delay
- Slant Range Error
- Incidence Angle Estimation
- PRF Fluctuation
Effect of Error
- Range Location
- Range Scale
- Azimuth Scale
- Internal Calibration
- Geometric Calibration
- Deskew
- Ground Projection
- Image Rotation
- Terrain Correction
Ground Control Point
DEM, DSM
Earth
- Earth Rotation
- Side-looking
- Target Height
Earth
- Azimuth Skew
- Range Non-Linearity
- Foreshortening, Layover,
Shadowing
Platform
- Inclination Angle
- Yaw Angel Error
- Pitch Angle Error
Platform
- Image Orientation Error
- Squint Angle
- Doppler Centroid

8. Multiple Target Distortion

9. Overview of Simulation
Selection of GCP
SAR images contain speckle noise. Thus, the GCPs that are used in EO (Electro Optical) images is no longer effective.
As a result, distinctive physical features on the ground that are readily identifiable from SAR image should be regarded as GCPs.
Examples: Runway, Intersection, Huge Building, cultivated land
Procedure
The first step is the selection and extraction of GCP from SAR and reference (EO) image. For the GCP extraction from satellite radar image, easily seen objects such as intersections and artificial structures with large RCS are chosen as reference points. Since the GCP(Ground Control Point) which is often used in EO images cannot be used for SAR Images due to speckle noise.
The second step is image transformation for GCP matching using the each extracted points of SAR and EO image.
In the last step, RMSE values are calculated comparing the geo-location error corrected image and EO image. 9

10. Proposed Algorithm Reference Image SAR Image
: Input the Same Region Image
Extract
Check Point
Extract GCP
Extract GCP, Check Point
: Extract the GCP, Check Point
Extract GCP Coordinates
Extract GCP Coordinates
: Extract GCP Coordinates
Image Transform
: Image Transform (Projective Transform)
Extract Check Point Coordinates
Extract Check Point Coordinates
Comparison
: Image Matching, Calculation of RMSE
Geo-located
SAR Image

11. [Error Corrected SAR Image]
Simulation Image
RADARSAT-1 1 2 4 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Seoul, Korea
Resolution : 10m x 10m
Error Corrected SAR Image (Using SAR General Information)
[Error Corrected SAR Image]

12. Before Error Correction
Before Using GCP 16 25 25 24 16 15 24 17 23 15 23 19 22 17 18 19 21 22 20 21 1 18 20
+ : EO Point (red) 1 2 2 4 14 14
+ : SAR Point (yellow) 4 12 13 12 13 3 3 11 11 7 7
RMSE [m]
[Easting, Northing]
350.05
RMSE [Overall] 5 5 6 6 8 8 9 9 10
[Before Using GCP] 10

13. After Error Correction (Using GCP)
After Error correction Image (Using GCP)
EO point(red)/SAR point(yellow) 16 25 24 15 17 23 19 22 18 21 20 1 2 14 4 12 13 3 11 7 5 6
RMSE [m]
[Easting, Northing]
-5.68
2.35
RMSE [Overall]
6.14 8 9 10
[After Error Correction]

14. Comparison of RMSE Northing : 454.05m Easting : 1212.20m
Overall : m
Northing : 2.49m
Easting : 5.87m
Overall : 6.38m

15. Simulation Results RMSE [m] Region Using SAR General Information
[Reference Image]
[Using SAR General Information]
[Using GCP]
RMSE [m]
Region
Using SAR General Information
Using GCP
Northing
Easting
Overall
Mountain Area
855.43
24.44
32.15
40.39
Urban Area
13.14
391.81
1.37
-6.96
7.12
Total
454.05
2.49
5.87
6.38

16. Conclusions
Geolocation error correction method is essential for SAR image utilization
New geo-location error correction method is proposed using GCP.
The performance of the proposed algorithm has been evaluated in terms of the RMSE distance by correcting foreshortening and layover and shadowing using the SAR image.
The proposed algorithm shows good performance in correcting a geo-location error with aid of GCP data compared to the case of the general SAR parameter information without GCP. Performance of the proposed method is improved especially in mountain area.

17. Thank you for your attention !
Contact author:
Prof. Young K. Kwag