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

Abstract

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.

The challenges of integrating explainable artificial intelligence into GeoAI

“…Matin and Pradhan (2021) employed SHAP values to explain, via a beeswarm plot, earthquake features within DNN. Even as methods are used in the explanation and interpretation of GeoAI (Ma et al, 2021), most visualization explanations are not georeferenced. However, these articles demonstrate that XAI methods like SHAP and visualization can be combined to improve explanations.…”

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

“…For the evaluation of the quality of the hexagon grid, two indicators were selected in this study: deviation index of spectral information (DISI) and mean root mean square error (RMSE) [14]. DISI compares the information loss pixel by pixel from a global perspective, while RMSE evaluates the geometric features of the image.…”

“…On the other hand, the hexagonal shape has the advantages of adjacency and topological consistency. In the field of remote sensing, relevant research has been carried out on hexagonal DGGS-based data modeling [13], hexagon accuracy evaluation [14], data fusion [15], and applications [16]. HexASCII [17], which is similar to the ESRI ACSII raster format, was proposed as an intermediate file to store DGGS data, and it defines specific rules to record the position of DGGS cells.…”

“…As for raster data, resampling methods have been adopted to extract pixel values from the DGGS cells at the closest resolution. Ma [14] compared the accuracy of three resampling methods using a planar aperture 4 hexagon grid system and pointed out that the bilinear resampling method had the highest accuracy. The above works are an exploration and attempt to use the advantages of DGGS to process and solve remote sensing data.…”