Optimisation of global grids for high-resolution remote sensing data
Optimisation of global grids for high-resolution remote sensing data
复制标题
DOI：
10.1016/j.cageo.2014.07.005
复制
发表时间：
2014-11-01
影响因子：
4.4
通讯作者：
Wagner, Wolfgang
中科院分区：
文献类型：
--
作者：
Bauer-Marschallinger, Bernhard;Sabel, Daniel;Wagner, Wolfgang
即将推出的卫星遥感系统将产生前所未有的大量空间数据，因此在存储和处理能力方面对处理设施提出了挑战。因此，有效处理遥感数据至关重要，需要对数据存储和操作的空间网格进行适当的定义。对于高分辨率图像数据，由地图投影定义的规则网格已被认为是可行的，并认识到其由于几何扭曲而存在的缺点。为此，我们定义了一个名为网格过采样因子（GOF）的新指标，用于估计将通用卫星图像投影到常规栅格网格期间出现的本地数据过采样。基于常见的地图投影，我们定义了一组经过优化的空间网格，以最大限度地减少数据过采样。此外，它们还确保任何位置都不会发生数据欠采样。根据所得的 GOF 值，我们得出结论，等距投影是最合适的，当使用七个大陆网格系统（以 Equi7 网格名称引入）时，全球平均过采样为 2%。与之前建议等面积投影的研究相反，我们分别推荐全球、半球和大陆网格的板块卡里投影、等距圆锥投影和等距方位角投影。 (C) 2014 Elsevier Ltd. 保留所有权利。
Upcoming remote sensing systems onboard satellites will generate unprecedented volumes of spatial data, hence challenging processing facilities in terms of storage and processing capacities. Thus, an efficient handling of remote sensing data is of vital importance, demanding a well-suited definition of spatial grids for the data's storage and manipulation. For high-resolution image data, regular grids defined by map projections have been identified as practicable, cognisant of their drawbacks due to geometric distortions. To this end, we defined a new metric named grid oversampling factor (GOF) that estimates local data oversampling appearing during projection of generic satellite images to a regular raster grid. Based on common map projections, we defined sets of spatial grids optimised to minimise data oversampling. Moreover, they ensure that data undersampling cannot occur at any location. From the resulting GOF-values we concluded that equidistant projections are most suitable, with a global mean oversampling of 2% when using a system of seven continental grids (introduced under the name Equi7 Grid). Opposed to previous studies that suggested equal-area projections, we recommend the Plate Carree, the Equidistant Conic and the Equidistant Azimuthal projection for global, hemispherical and continental grids, respectively. (C) 2014 Elsevier Ltd. All rights reserved.