SIFT特征匹配无人飞艇多光谱影像拼接

无人飞艇抗风能力弱、稳定性差且不符合航摄规范，采用传统方法对其所获取的影像进行拼接往往达不到较高的精度. 为此，该文提出一种基于尺度不变的特征变换进行多光谱遥感影像特征匹配的拼接. 将多光谱信息引入SIFT特征向量集，采用BBF(best-bin-first)算法和随机抽样一致性方法进行粗、精匹配处理和误差剔除，以SIFT特征匹配计算的最优变换矩阵实现光谱影像拼接. 对无人飞艇获取的多光谱影像拼接实验结果表明，所提出的方法能获取大量匹配特征点，且影像间的变换矩阵稳健，光谱影像拼接精度和效果能满足判读解译的需求.

Mosaicing of Multiple Spectrum Images Acquired from Unmanned Airship with SIFT Feature Matching

A multi-spectral remote sensing image mosaic technique with scale invariant feature transform (SIFT) feature matching is proposed to deal with images obtained from an unmanned airship. The acquired pictures usually do not meet the specifications of aerial photography because the airship is unstable in wind. We propose to use SIFT feature vectors with spectral information to improve robustness of the mosaicing algorithm. The BBF(best-bin-first) algorithm and RANSAC（random sample consensus）methods are used for coarse and fine matching processing, and error removal. The optimal transformation matrix from SIFT feature matching calculation is used to achieve image mosaicing. Experimental results show that the algorithm can produce a large number of matching feature points to obtain a stable transformation matrix for further image mosaicing, with accuracy that meets the needs of image interpretation.