Spatial Big Data Infrastructure for the Japanese Hyper-spectral Imager SUIte (HISUI) based on World Grid Square Statistics

This study proposes a framework to manage HISUI data (spatiotemporal and spectral big data) as spatial statistics regarding time and spectrum. This study focuses on spatial Big Data infrastructure for developing and deploying interactive Web data applications of HISUI hyperspectral data from a constructive point of view.The study aims to construct a system for spatiotemporal spectrum analysis and visualization based on the proposed technique. Such a spatial Big Data infrastructure assists in developing algorithms that enable users to interactively analyze, classify, and detect temporal changes on the Earth’s surface. As a result, users can conduct statistical analysis of spatio-temporal and spectral characteristics on hyperspectral images.The World Grid Square statistics on descriptive statistics of reflectances for each band are produced by parallel computation with high-speed calculation methods and provided via Web API on MESHSTATS. The response is formatted as CSV with World Grid Square code, sample size, and descriptive statistics selected by type for 185 bands.The proposed method enables users to analyze hyperspectral satellite data captured as multi-channel raster data with different observation directions, times, and weather conditions by converted into Grid Square statistics. It is concluded that our data analytics platform enables users to conduct analysis on Grid Square statistics about HISUI hyperspectral satellite data with statistical significance.