An investigation towards wavelet based optimization of automatic image registration techniques
This work addresses challenges in remote sensing applications like change detection and image fusion, but appears incremental as it builds on existing SIFT methods.
This research investigated enhancing automatic image registration techniques for high-resolution remote sensing data using wavelet transforms, finding that combining SIFT feature-based methods with wavelet enhancement yielded the best results.
Image registration is the process of transforming different sets of data into one coordinate system and is required for various remote sensing applications like change detection, image fusion, and other related areas. The effect of increased relief displacement, requirement of more control points, and increased data volume are the challenges associated with the registration of high resolution image data. The objective of this research work is to study the most efficient techniques and to investigate the extent of improvement achievable by enhancing them with Wavelet transform. The SIFT feature based method uses the Eigen value for extracting thousands of key points based on scale invariant features and these feature points when further enhanced by the wavelet transform yields the best results.