The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B5-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 69–75, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-69-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 69–75, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-69-2020

  24 Aug 2020

24 Aug 2020

AN INTEGRATED MINERAL SPECTRAL LIBRARY USING SHARED DATA FOR HYPERSPECTRAL REMOTE SENSING AND GEOLOGICAL MAPPING

B. S. Xie1,2, S. Y. Zhou1,2, and L. X. Wu1,2 B. S. Xie et al.
  • 1School of Geosciences and Info-Physics, Central South University, Changsha, China
  • 2Laboratory of Geo-Hazards Perception, Cognition and Predication, Central South University, Changsha, China

Keywords: Mineral Spectral Library, Shared Data, Data Cleaning, Spectrum Classifier, Spectral Match, Landcover Identification

Abstract. Mineral spectral library (MSL) is the foundation of hyperspectral remote sensing, and a significant tool of storing and managing massive mineral spectral data to facilitate the matching or identifying of unknown rocks and minerals conveniently and fast. However, mineral spectral data are scattered and stored in different spectral libraries worldwide, which behave different spectral resolutions, mineral categories and measurement parameters, and hinder its application in field investigation, mineral identification, landcover identification and geological mapping. An integrated MSL using shared data is developed currently in Central South University, China, to improve the properties of MSL. We collected the shared spectral data and related information (e.g., mineral attribute data, spectrometer information, etc.) worldwide, performed data cleaning measures to retain the qualified spectral data and consolidated all the data in a common framework so as to establish a reliable and comprehensive dataset, and developed an integrated MSL for data management and diversified applications. The user can analysis the target spectrum with the spectrum absorption characteristic parameters, and match the measured spectral curve with the reference spectrum in the integrated MSL to find the most similar spectrum curve. It’s crucial to note that a new spectrum classifier was designed to limit the scope of matching for improving the efficiency of identification when the experimental sample lacks the specific information. The integrated MSL is developed in B/S and C/S website environments. A demonstration of functions of the integrated MSL and its preliminary applications are introduced in the article.