利用光谱角敏感森林的高光谱数据快速匹配方法

A Fast Spectral Matching Algorithm for Larger-Scale Hyperspectral Data:Spectral Angle Sensitive Forest

提出了一种新的光谱匹配算法——光谱角敏感森林方法。在位置敏感哈希函数算法的基础上引入光谱角度量,并利用新的数据桶检索结构改进了原位置敏感哈希函数算法中部分目标光谱点无法得到匹配光谱的缺陷。理论和实验证明,光谱角敏感森林算法的计算效率较传统高维数据匹配方法有较明显优势。

We propose a spectral matching algorithm,spectral angle sensitive forest（SASF）,which improves the spectral matching efficiency in high dimensional large-scale hyperspectral dataset.The locality sensitive hashing（LSH） is expanded to the metric space of spectral angle.Moreover,we introduce a new scheme to index the data bucket,which remove the flaw of the original LSH method that a part of the query points won＇t get any neighbors.We provide systematical analysis of the parameters,and theoretical and experimental evaluation of the algorithm.The computational efficiency of SASF is proved outperforming the former algorithms.And SASF also provides a tradeoff between efficiency and precision of spectral matching by which make the user has more choices in the applications.