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

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

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.

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]

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

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

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

Multiple Target Distortion

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

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

[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]

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] -1757.88 350.05 RMSE [Overall] 1792.39 5 5 6 6 8 8 9 9 10 [Before Using GCP] 10

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]

Comparison of RMSE Northing : 454.05m Easting : 1212.20m Overall : 1294.45m Northing : 2.49m Easting : 5.87m Overall : 6.38m

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 -3807.3 3902.24 24.44 32.15 40.39 Urban Area 13.14 -391.59 391.81 1.37 -6.96 7.12 Total 454.05 1212.20 1294.45 2.49 5.87 6.38

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.

Thank you for your attention ! Contact author: Prof. Young K. Kwag ykwag@kau.ac.kr