Wavelet-based image registration technique for high-resolution remote sensing images
Computers & Geosciences
Fast phase-based registration of multimodal image data
Signal Processing
A multi-modal automatic image registration technique based on complex wavelets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Remote sensing image registration techniques: a survey
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
A hybrid approach based on MEP and CSP for contour registration
Applied Soft Computing
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Pattern Recognition
| Hi-index | 0.01 |
The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.