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。
With the development of remote sensing satellite technology, the temporal and spatial resolution of remote sensing data has been continuously improved, showing the trend of big data and massive quantification, which has brought challenges to the screening of remote sensing data. Traditional remote sensing data retrieval often has the problem of large amount of query results and high overlap. It requires manual data selection, which is inefficient and low in accuracy. Therefore, how to quickly and accurately find the required data from a large number of remote sensing images is a problem that needs to be solved urgently. In this paper, a remote sensing data screening algorithm based on area coverage is used to divide the target area into non-overlapping fragments using the effective range of images. A normalized mathematical calculation model is established based on the number of fragments contained in the image, imaging time, and cloud cover. The model obtains a comprehensive cost. An optimal image combination is selected according to the cost, which completely covers the target area. This paper confirms the effectiveness of the method through Landsat8 data, and improves the screening efficiency through parallel computing.
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