测绘科学技术学报2025，Vol.41Issue(6)：589-596,8.DOI:10.3969/j.issn.1673-6338.2025.06.006
一种基于网格剖析的遥感影像数据优选方法
A Method of Remote Sensing Image Data Optimization Based on Discrete Grid System
摘要
Abstract
With the rapid advancement of remote sensing satellites,especially small ones,the data resources of re-mote sensing satellites have increased significantly.The existing spatial retrieval methods encounter issues such as high redundancy,high complexity,slow speed,and even difficulties in completion when screening large-scale re-mote sensing data coverage,which restrict the rapid application and service of remote sensing data resources.Hence,based on the global spatial grid subdivision framework,this paper designs a fast gridding method for two-dimensional polygon data and a scoring parallel algorithm to screen a small number of images and achieve maxi-mum regional coverage.Verified through two sets of experiments:the spatial splitting efficiency comparison experi-ment and the preferred image result comparison experiment,the gridding method is less influenced by the number of images in terms of splitting time consumption,but there are more elements and the accuracy is negatively corre-lated with the number of elements.The solution efficiency of the algorithm is,on average,35％higher than that of the greedy algorithm and 42.8 times higher than that of the random pit-jumping algorithm.The number of preferred images is 5％more than that of the random pit-jumping algorithm and 5％less than that of the greedy algorithm.Therefore,the method proposed in this paper can effectively address the problem of remote sensing image optimiza-tion,especially for large-scale data.It has low splitting time consumption and solution time consumption,and good solution effect,providing a valuable reference for practical applications.关键词
遥感影像/集合覆盖/全球空间网格/评分并行/迭代去重Key words
remote sensing image/set coverage problem/global spatial grid/iterative de-duplication分类
天文与地球科学引用本文复制引用
陈雪华,孙月坤,王刚,童晓冲,周箭..一种基于网格剖析的遥感影像数据优选方法[J].测绘科学技术学报,2025,41(6):589-596,8.基金项目
嵩山实验室项目(纳入河南省重大科技管理体系)(221100211000-03) （纳入河南省重大科技管理体系）
河南省自然科学基金项目(212300410096) （212300410096）
国家重点研发计划项目(2018YFB0505304). （2018YFB0505304）
