Piecewise cubic mapping functions for image registration
Pattern Recognition
Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Solution of the Radial Basis Function Interpolation Equations: Domain Decomposition Methods
SIAM Journal on Scientific Computing
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Fast parametric elastic image registration
IEEE Transactions on Image Processing
Mesh Topological Optimization for Improving Piecewise-Linear Image Registration
Journal of Mathematical Imaging and Vision
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In remote sensing, because of the wide diversity of image characteristics (size, spatial and radiometric resolution, terrain relief, observation poses, etc.), image registration methods that work well on certain satellite images may not produce acceptable results for others, requiring more powerful techniques. A variety of registration techniques that account for images with non-rigid geometric deformations have been proposed, including piecewise (linear or cubic) functions, weighted mean functions, radial basis functions, B-spline functions, etc. This paper compares three of them: polynomial, piecewise-linear and thin-plate-spline functions, and analyses their performance under a variety of factors: off-nadir viewing, terrain relief, density of control points, and 3D geometric correction. Our comparison applies on panchromatic QuickBird imagery, both ortho-ready (as provided by DigitalGlobe) and orthorectified, acquired on different dates, from different observation attitudes, and sensing different land covers: urban area, high-relief terrain, and a combination of both.