Comparison of Orbit-Based and Time-Offset-Based Geometric Correction Models for SAR Satellite Imagery Based on Error Simulation

Geometric correction of SAR satellite imagery is the process to adjust the model parameters that define the relationship between ground and image coordinates. To achieve sub-pixel geolocation accuracy, the adoption of the appropriate geometric correction model and parameters is important. Until now, various geometric correction models have been developed and applied. However, it is still difficult for general users to adopt a suitable geometric correction models having sufficient precision. In this regard, this paper evaluated the orbit-based and time-offset-based models with an error simulation. To evaluate the geometric correction models, Radarsat-1 images that have large errors in satellite orbit information and TerraSAR-X images that have a reportedly high accuracy in satellite orbit and sensor information were utilized. For Radarsat-1 imagery, the geometric correction model based on the satellite position parameters has a better performance than the model based on time-offset parameters. In the case of the TerraSAR-X imagery, two geometric correction models had similar performance and could ensure sub-pixel geolocation accuracy

Estimation and improvement in the geolocation accuracy of rational polynomial coefficients with minimum GCPs using KOMPSAT-3A

In this paper, we propose a method to regenerate Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery and to reduce the geolocation error using minimum ground control points (GCPs). To estimate the new RPCs, the physical sensor model fitted to KOMPSAT-3A imagery was utilized and virtual GCPs over the study area were created. The size of the virtual grid used was 20x20x20. To remove the sensor-related errors in physical sensor model, three different image correction models (image coordinate translation model, shift and drift model, and affine transformation model) were additionally applied. We evaluated our proposed method in two areas within Korea, one in urban (Seoul) and one in rural (Goheung) areas. The results showed that there was a significant improvement after applying the suggested approach in the two areas. The image coordinate translation model is suggested in terms of GCP requirement and expected errors estimated from the error propagation analysis using Gauss–Markov Model (GMM)

Error budget analysis of geocoding and geometric correction for KOMPSAT-5 SAR imagery

Geocoding geometrically rectifies a remote sensing image according to a specific map projection, and is an essential process for utilizing synthetic aperture radar (SAR) satellite images. The accuracy of geocoding is affected by various intercorrelated error sources. In this study, we propose a framework for improving the geocoding accuracy of SAR images. Our framework consists of two major theoretical and computational steps: 1) calculating and setting the error budget of the SAR image geocoding accuracy and checking their quality and 2) applying a geometric correction model if the quality is lower than the predefined threshold. Error budget analysis was performed by utilizing the law of variance propagation, considering the correlations among the primary error sources. During the second (geometric correction) step, the non-multicollinearity (N-MC) model, a ground control point (GCP)-based geometric correction model without multicollinearity, was proposed. Experiments were conducted using two KOMPSAT-5 SAR images from Daejeon City, Korea to verify the framework. The geocoding accuracy of SAR images #1 and #2 exceeded the error budgets of all confidence levels, except for the row direction of SAR image #2, despite the vendor’s internal calibration. In the second step, two SAR images were geometrically corrected by applying the N-MC model. The use of geometric correction improved the geocoding accuracy of the two SAR images by approximately two to five pixels in the row and column directions. The final geocoding accuracy of the SAR images was within the error budget

Utilization of a Terrestrial Laser Scanner for the Calibration of Mobile Mapping Systems

This paper proposes a practical calibration solution for estimating the boresight and lever-arm parameters of the sensors mounted on a Mobile Mapping System (MMS). On our MMS devised for conducting the calibration experiment, three network video cameras, one mobile laser scanner, and one Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) were mounted. The geometric relationships between three sensors were solved by the proposed calibration, considering the GNSS/INS as one unit sensor. Our solution basically uses the point cloud generated by a 3-dimensional (3D) terrestrial laser scanner rather than using conventionally obtained 3D ground control features. With the terrestrial laser scanner, accurate and precise reference data could be produced and the plane features corresponding with the sparse mobile laser scanning data could be determined with high precision. Furthermore, corresponding point features could be extracted from the dense terrestrial laser scanning data and the images captured by the video cameras. The parameters of the boresight and the lever-arm were calculated based on the least squares approach and the precision of the boresight and lever-arm could be achieved by 0.1 degrees and 10 mm, respectively