A Precision Evaluation Index System for Remote Sensing Data Sampling Based on Hexagonal Discrete Grids

• Main Authors: Yue Ma, Guoqing Li, Xiaochuang Yao, Qianqian Cao, Long Zhao, Shuang Wang, Lianchong Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: remote sensing; global discrete grid; accuracy evaluation; hexagonal grid
• Online Access: https://www.mdpi.com/2220-9964/10/3/194

With the rapid development of earth observation, satellite navigation, mobile communication, and other technologies, the order of magnitude of the spatial data we acquire and accumulate is increasing, and higher requirements are put forward for the application and storage of spatial data. As a new form of data management, the global discrete grid can be used for the efficient storage and application of large-scale global spatial data, which is a digital multiresolution georeference model that helps to establish a new model of data association and fusion. It is expected to make up for the shortcomings in the organization, processing, and application of current spatial data. There are different types of grid systems according to the grid division form, including global discrete grids with equal latitude and longitude, global discrete grids with variable latitude and longitude, and global discrete grids based on regular polyhedrons. However, there is no accuracy evaluation index system for remote sensing images expressed on the global discrete grid to solve this problem. This paper is dedicated to finding a suitable way to express remote sensing data on discrete grids, as well as establishing a suitable accuracy evaluation system for modeling remote sensing data based on hexagonal grids to evaluate modeling accuracy. The results show that this accuracy evaluation method can evaluate and analyze remote sensing data based on hexagonal grids from multiple levels, and the comprehensive similarity coefficient of the images before and after conversion is greater than 98%, which further proves the availability of the hexagonal-grid-based remote sensing data of remote sensing images. This evaluation method is generally applicable to all raster remote sensing images based on hexagonal grids, and it can be used to evaluate the availability of hexagonal grid images.