Remote sensing image registration approach based on a retrofitted SIFT algorithm and Lissajous-curve trajectories.
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.
Song
Zhi-li
ZL
School of Computer Science, Fudan University, Shanghai, China. zlsong@fudan.edu.cn
Li
Sheng
S
George
Thomas F
TF
D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000379:methods*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique