Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching
Top Cited Papers
- 5 December 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 14 (1), 3-7
- https://doi.org/10.1109/lgrs.2016.2600858
Abstract
The scale-invariant feature transform algorithm and its many variants are widely used in feature-based remote sensing image registration. However, it may be difficult to find enough correct correspondences for remote image pairs in some cases that exhibit a significant difference in intensity mapping. In this letter, a new gradient definition is introduced to overcome the difference of image intensity between the remote image pairs. Then, an enhanced feature matching method by combining the position, scale, and orientation of each keypoint is introduced to increase the number of correct correspondences. The proposed algorithm is tested on multispectral and multisensor remote sensing images. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the number of correct correspondences and aligning accuracy.Keywords
This publication has 16 references indexed in Scilit:
- Multispectral Image Alignment With Nonlinear Scale-Invariant Keypoint and Enhanced Local Feature MatrixIEEE Geoscience and Remote Sensing Letters, 2015
- An Efficient SIFT-Based Mode-Seeking Algorithm for Sub-Pixel Registration of Remotely Sensed ImagesIEEE Geoscience and Remote Sensing Letters, 2014
- A Novel Point-Matching Algorithm Based on Fast Sample Consensus for Image RegistrationIEEE Geoscience and Remote Sensing Letters, 2014
- SAR-SIFT: A SIFT-Like Algorithm for SAR ImagesIEEE Transactions on Geoscience and Remote Sensing, 2014
- Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imageryApplied Mathematics and Computation, 2014
- Fast SIFT algorithm based on Sobel edge detectorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- BRISK: Binary Robust invariant scalable keypointsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing ImagesIEEE Transactions on Geoscience and Remote Sensing, 2011
- Robust Scale-Invariant Feature Matching for Remote Sensing Image RegistrationIEEE Geoscience and Remote Sensing Letters, 2009
- Random sample consensusCommunications of the ACM, 1981