关键词: 高维索引, 相似性查询, 遥感光谱数据库

Abstract: Remote sensing hyperspectral data is a typical high-dimensional data with the character of spatial clustering.In the paper，to index this kind of database，the Pyramid Technology（PT） is improved for the application to the mechanism of iDistance indexing.Based on the two techniques above，an optimal index is proposed，called SP-iDistance（Spherical Pyramid based iDistance）.SP-iDistance is a high-dimensional indexing structure in metric space，and adopts two-level spatial partition.The distance from point object to reference point is combined with the SP number which is used to identify the space orientation information to calculate the one-dimensional value mapping from the hyperspectral data.When processing the spectral similarity query，the distance and space direction searching can be accomplished simultaneously.The experimental results demonstrate that SP-iDistance can more efficiently reduce the distance computation and I/O，and has higher filtering efficiency than PT or iDistance indexing individually.It can be used for the real-time applications on hyperspectral data matching.

Key words: high-dimensional indexing, similarity query, remote sensing spectral library