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| 1 | 遥感影像区域覆盖数据集筛选方法显示文摘随着遥感卫星技术的发展,遥感数据的时空分辨率不断提升,呈现出大数据、海量化的趋势,为遥感数据的筛选工作带来了挑战。传统的遥感数据检索往往出现查询结果数据量大、重叠度高的问题,需要人工进一步挑选数据,效率低且准确度不高,因此如何从大量遥感影像中快速准确地找到所需数据是目前亟需解决的问题。采用区域覆盖数据集筛选算法,利用影像的有效范围将目标区域分割成互不重叠的碎块,根据影像包含的碎块数量、成像时间和云量等参数建立归一化数学计算模型,得到一个综合代价,根据代价的大小筛选出最优影像组合,将目标区域完全覆盖。通过Landsat8数据证实了方法的有效性,并通过并行计算提升了筛选效率。 | 闫雪静 刘巍 刘士彬 段建波 夏玮 | 2023 | 中国科学院大学学报(中英文)2023，40，4： | 0 |
| 2 | An improved coverage-oriented retrieval algorithm for large-area remote sensing data显示文摘With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data. | Xuejing Yan Shibin Liu Wei Liu Qin Dai | 2022 | International Journal of Digital Earth2022，15，1： | 0 |
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