Song Zhi-li, Li Sheng, George Thomas F
School of Computer Science, Fudan University, Shanghai, China.
Opt Express. 2010 Jan 18;18(2):513-22. doi: 10.1364/OE.18.000513.
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.
通过对尺度不变特征变换（SIFT）描述符进行改进，并基于李萨如曲线生成的轨迹开发一种新的相似性度量函数，构建了一种新的遥感图像配准方法，该方法比以前的方法更稳健、更准确。在特征匹配正确率低于20%的复杂情况下，改进后的SIFT描述符将正确率提高到近100%。大多数情况下，相似性度量函数使得对同一地理位置的瞬时变化进行定量分析成为可能。
