Title of Thesis

Level-3 Geometric Correction of FORMOSAT-2 Imagery and Efficient Image Resampling

Author(s)

Faheem Arif

Abstract
A significant problem in satellite imagery is geometric distortion. Accurate remote sensing and high resolution satellite images have made it necessary to revise the geometric correction techniques used for ortho-rectification. Conventional methods of photogrammetric modeling of remotely sensed images are insufficient for mapping purposes and need to be substituted with more rigorous approach to get a true orthophoto.
FORMOSAT-2, a newly launched remote sensing Taiwanese satellite, has high spatial resolution sensor onboard for a daily revisit orbit. However, like any image acquisition system, it also produces geometric distortions in its raw images. Pixel Projection Model (PPM) was devised by National Space Program Office (NSPO) Taiwan, for processing of Level-1A (Raw) satellite images to Level-2 (radio metrically corrected) images. Being systematically corrected, Level-2 images still possess terrain elevation, rotation-translation and geometric distortions. There was a dire need for enhancement of this model to produce Level-3 (geometrically corrected) image products.
A novel method for Level-3 correction of satellite images, especially suited for FORMOSAT-2, has been developed. The PPM has been enhanced to cater for geometric distortions caused by the attitude change in the satellite specifically in the pre-processing stage. The three attitude angles of the satellite are thus calculated and corrected as per the ground position or coordinates using least squares adjustments. The approach is based on non-systematic method in which physical modeling of the satellite imagery is considered. The mathematical model has been developed to calculate and correct instrument bias/ attitude angles. Ground Control Points have been integrated in the algorithm besides vertex matching for more precise results. Results were verified by computing MSE for image to image matching and point to point matching. An improvement of 86.3% was obtained for the new Level-3 correction technique over the existing Level-2 algorithm.
Three conventional interpolation techniques for transformation of image pixels to earth coordinate system were also analyzed for improvement. The experimental results show that the cubic convolution based modeling is best suited for output pixel value transformation but it is computationally complex with a higher execution time. To improve this, a wavelettransform based filter (Daubechies 4) was developed for image pixel transformation. The new method provides similar visual interpretation as cubic convolution but with much lower computational complexity and execution time. The proposed wavelet-transform based method is an order of magnitude faster than the cubic interpolation technique.
Level-3 geometrically corrected FORMOSAT-2 images can be used for disaster investigation/ prediction, environmental monitoring, vegetation evaluation, and multitemporal image matching. In our work, we have focused on the application of geometrically corrected imagery for disaster investigation.

1

1.1 Premise

1.2 Geometric Distortions in Satellite Imagery
1.3 Problem Statement and Research Objectives
1.4 Contribution of This Research
1.5 Outline of the Thesis

2.1 Introduction

2.2 Computing the Parameters of a Two-dimensional Coordinate Transformations
2.3 Filling an Array Aligned with the Ground Coordinate System
2.4 Mathematical Relationship between Coordinate Systems
2.5 Selection of Ground Control Points
2.6 Summary

3.1 Introduction

3.2 FORMOSAT-2 Satellite

3.3 Factors Affecting Satellite Image Geometry

3.4 Pixel Projection Modeling

3.5 Mathematical Formulation of PPM

3.6 Summary

4.1 Introduction

4.2 Test Images and Ancillary Data
4.3 Level-3 Geometric Correction Technique
4.4 Improvements Achieved in PPM

4.5 Summary

5.1 Introduction

5.2 Residual Analysis
5.3 Image Resampling
5.4 Application to Disaster Investigation

5.5 Summary

6.1 Overview

6.2 Achievements and Applications
6.3 Limitations

6.4 Recommendations and Future Research

6.5 Summary

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