Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1157-1160, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1157-1160, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1157-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

MINERAL INFORMATION EXTRACTION BASED ON GAOFEN-5’S THERMAL INFRARED DATA

L. Liu and K. Shang L. Liu and K. Shang
  • China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing, China

Keywords: Gaofen-5, thermal infrared, emissivity spectra, mineral information extraction, image simulation

Abstract. Gaofen-5 carries six instruments aimed at various land and atmosphere applications, and it’s an important unit of China High-resolution Earth Observation System. As Gaofen-5’s thermal infrared payload is similar to that of ASTER, which is widely used in mineral exploration, application of Gaofen-5’s thermal infrared data is discussed regarding its capability in mineral classification and silica content estimation. First, spectra of silicate, carbonate, sulfate minerals from a spectral library are used to conduct spectral feature analysis on Gaofen-5’s thermal infrared emissivities. Spectral indices of band emissivities are proposed, and by setting thresholds of these spectral indices, it can classify three types of minerals mentioned above. This classification method is tested on a simulated Gaofen-5 emissivity image. With samples acquired from the study area, this method is proven to be feasible. Second, with band emissivities of silicate and their silica content from the same spectral library, correlation models have been tried to be built for silica content inversion. However, the highest correlation coefficient is merely 0.592, which is much lower than that of correlation model built on ASTER thermal infrared emissivity. It can be concluded that GF-5’s thermal infrared data can be utilized in mineral classification but not in silica content inversion.