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Link to original content: http://www.ncbi.nlm.nih.gov/pubmed/24021971
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. 2013 Sep 9;13(9):12030-43.
doi: 10.3390/s130912030.

A study on rational function model generation for TerraSAR-X imagery

Akram Eftekhari  1 , Mohammad Saadatseresht, Mahdi Motagh
Affiliations

A study on rational function model generation for TerraSAR-X imagery

Akram Eftekhari et al. Sensors (Basel). .

Abstract

The Rational Function Model (RFM) has been widely used as an alternative to rigorous sensor models of high-resolution optical imagery in photogrammetry and remote sensing geometric processing. However, not much work has been done to evaluate the applicability of the RF model for Synthetic Aperture Radar (SAR) image processing. This paper investigates how to generate a Rational Polynomial Coefficient (RPC) for high-resolution TerraSAR-X imagery using an independent approach. The experimental results demonstrate that the RFM obtained using the independent approach fits the Range-Doppler physical sensor model with an accuracy of greater than 10-3 pixel. Because independent RPCs indicate absolute errors in geolocation, two methods can be used to improve the geometric accuracy of the RFM. In the first method, Ground Control Points (GCPs) are used to update SAR sensor orientation parameters, and the RPCs are calculated using the updated parameters. Our experiment demonstrates that by using three control points in the corners of the image, an accuracy of 0.69 pixels in range and 0.88 pixels in the azimuth direction is achieved. For the second method, we tested the use of an affine model for refining RPCs. In this case, by applying four GCPs in the corners of the image, the accuracy reached 0.75 pixels in range and 0.82 pixels in the azimuth direction.

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Figures

Figure 1.
Figure 1.
General workflow of developing a rational functional model [11]. SAR: synthetic aperture radar, RFM: rational function model, GCP: ground control point, RPC: rational polynomial coefficient.
Figure 2.
Figure 2.
Distributions of ground control points (GCPs) and check points (CKPs) in the study area for a scenario involving five GCPs. Note: triangles represent the GCPs, and circles represent the CKPs.
Figure 3.
Figure 3.
An example of a ground control point (GCP) selected in (a) the synthetic aperture radar (SAR) image and (b) the optical image.
Figure 4.
Figure 4.
Plot of rational function modell (RFM) fitting errors versus number of elevation layers.
Figure 5.
Figure 5.
Discrepancies in the image space between the calculated rational polynomial coefficient (RPCs) and the true ground control points (GPCs).
See this image and copyright information in PMC

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