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

Akram Eftekhari¹, Mohammad Saadatseresht, Mahdi Motagh

¹Department of Surveying and Geomatics Engineering, University of Tehran, Tehran 14395-515, Iran. akrameftekhary@gmail.com.

Sensors (Basel, Switzerland)

|September 12, 2013

Summary

This study validates the Rational Function Model (RFM) for Synthetic Aperture Radar (SAR) imagery, achieving sub-pixel accuracy using Ground Control Points (GCPs) and affine refinement for improved geolocation.

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Area of Science:

Photogrammetry
Remote Sensing
Geomatics Engineering

Background:

The Rational Function Model (RFM) is a common approximation for rigorous sensor models in optical remote sensing.
Its applicability to Synthetic Aperture Radar (SAR) imagery, specifically high-resolution TerraSAR-X, remains underexplored.
Accurate geometric processing of SAR data is crucial for various applications.

Purpose of the Study:

To investigate the generation of Rational Polynomial Coefficients (RPCs) for high-resolution TerraSAR-X imagery using an independent approach.
To evaluate the accuracy of the RFM when applied to SAR data.
To explore methods for improving the geometric accuracy of SAR-based RFMs.

Main Methods:

Generating independent RPCs for TerraSAR-X imagery.
Fitting the independent RFM to the Range-Doppler physical sensor model.
Improving geometric accuracy using Ground Control Points (GCPs) via sensor orientation parameter updates.
Refining RPCs using an affine transformation model with GCPs.

Main Results:

The independently generated RFM fits the Range-Doppler model with accuracy better than 10^-3 pixels.
Using three GCPs improved accuracy to 0.69 pixels (range) and 0.88 pixels (azimuth).
Employing an affine model with four GCPs achieved accuracy of 0.75 pixels (range) and 0.82 pixels (azimuth).

Conclusions:

The RFM is a viable alternative to rigorous sensor models for high-resolution SAR imagery.
Independent RPC generation and subsequent refinement with GCPs significantly enhance geometric accuracy.
The study provides practical methods for improving SAR geolocation using RFM-based approaches.