Open AccessRemote sensing image registration approach based on a retrofitted SIFT algorithm and Lissajous-curve trajectories
Author(s) -
Zhijian Song,
Sheng Li,
Thomas F. George
Publication year - 2010
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.18.000513
Subject(s) - scale invariant feature transform , lissajous curve , artificial intelligence , image registration , computer vision , computer science , similarity measure , similarity (geometry) , feature (linguistics) , image processing , position (finance) , pattern recognition (psychology) , image (mathematics) , mathematics , geometry , linguistics , philosophy , finance , economics
Through retrofitting the descriptor of a scale-invariant feature transform (SIFT) and developing a new similarity measure function based on trajectories generated from Lissajous curves, a new remote sensing image registration approach is constructed, which is more robust and accurate than prior approaches. In complex cases where the correct rate of feature matching is below 20%, the retrofitted SIFT descriptor improves the correct rate to nearly 100%. Mostly, the similarity measure function makes it possible to quantitatively analyze the temporary change of the same geographic position.