【摘要】 随着对地观测技术的日新月异,遥感数据已形成大数据、海量化的发展趋势.面对海量遥感数据的应用需求,如何快速、准确地查找到所需数据是目前遥感数据组织管理研究的问题之一,而在众多遥感数据查找方式中,针对某一地区单时相全覆盖影像数据集筛选又是遥感数据应用过程的重要一环.然而,目前国内外主要遥感数据服务平台都缺乏相关工具,单时相全覆盖影像数据集的筛选主要还是通过人工方式完成,不仅效率极低,还容易造成遗漏等问题.因此,本文结合实际需求,提出了一种基于空间二次过滤的遥感数据单时相全覆盖检索方法,通过对比实验,该方法在低云量数据充分的情况下,能够自动、快速、准确地筛选出目标区域最新单一时相全覆盖遥感影像数据集,具有很好的实用意义.更多还原

【Abstract】 With the rapid development of the earth observation technology,remote sensing data has become more and more large.In the face of massive remote sensing data applications,how quickly,accurately find the required data is one of the key problems in the remote sensing data organization and management research.In many remote sensing data searching operations,a single time phase and full coverage of the image data is generally an inevitable step in the process of application of remote sensing data.However,there is not any convenient tool for such data searching operation,especially in the major domestic and foreign service platforms of remote sensing data,which are mainly through the artificial way,however the artificial way is not only low efficiency,but also high missing error rate.In this work,the retrieval method for single time phase and full coverage searching in massive remote sensing data was proposed based on space secondary filter.The contrast experiments show that the method works much more automatically,quickly and accurately in sufficient remote sensing data with low coverage rate of cloud.更多还原