Statistical method of remote sensing image application area based on graphic metadata

Major nationwide monitoring projects usually involve the integration and analysis of a large number of satellite images. When counting the application area of large-scale remote sensing images covering the whole country, it is usually easy to take into account the overlapping coverage, invalid value coverage, and cloud coverage area, which makes the statistical results have redundancy problems. To address this problem, the paper proposes an optimal image screening model and a statistical method for remote sensing image application area based on graphical metadata. For the overlapping coverage regions. The optimal remote sensing image is selected through three factors: image spatial resolution, date difference between image photting time and a unified time point, and the proportion of effective coverage area of the image. The graphic metadata of polygon vector structure is used to record the spatial range and attribute information of each selected remote sensing image block, and based on which the actual application area of large-scale remote sensing images is calculated and counted. Experimental results showed that this method, through redundancy deduction, could accurately calculate the application area of remote sensing images at various spatial resolution levels and plotting time periods nationwide, which would provide data reference for overall planning and scientific decision-making of remote sensing image acquisition.