CAJ | 학술논문

遥感高光谱数据是一种具有空间聚集特性的高维数据.对PT方法进行改进使之与iDistance的索引机制相适应,并融合这两种不同的空间划分策略,提出一种适用于高光谱数据的索引结构.该索引是一种度量空间的高维索引,采用两级空间划分,在处理光谱相似性查询时可同时完成针对距离和空间方位的数据过滤.实验证明该索引可以有效降低I/O和距离计算次数,具有较高的剪枝效率,适用于高光谱数据相似性查询.
요감고광보수거시일충구유공간취집특성적고유수거.대PT방법진행개진사지여iDistance적색인궤제상괄응,병융합저량충불동적공간화분책략,제출일충괄용우고광보수거적색인결구.해색인시일충도량공간적고유색인,채용량급공간화분,재처리광보상사성사순시가동시완성침대거리화공간방위적수거과려.실험증명해색인가이유효강저I/O화거리계산차수,구유교고적전지효솔,괄용우고광보수거상사성사순.
Remote sensing hyperspectral data is a typical high-dimensional data with the character of spatial clustering.In the pa per,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 iDis-tance).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.