ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 649–654, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-649-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 649–654, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-649-2020

  03 Aug 2020

03 Aug 2020

A COMPARATIVE ANALYSIS OF SPECTRAL INFORMATION EXTRACTION ALGORITHMS FOR TARGET DETECTION OF HYDROTHERMAL ALTERATION ZONES USING ASTER SATELLITE IMAGE DATA

A. J. Abubakar1, M. Hashim2, A. B. Pour3, and Y. Saleh1 A. J. Abubakar et al.
  • 1Department of Geography, Faculty of Science, Kaduna State University P. M. B. 2339, Kaduna, Nigeria
  • 2Geoscience and Digital Earth Centre (INSTeG), Research Institute for Sustainable Environment (RISE), Universiti Teknologi Malaysia (UTM), Malaysia
  • 3Korea Polar Research Institute (KOPRI), Songdomirae-ro, Yeonsu-gu, Incheon 21990, Republic of Korea

Keywords: ASTER, Hydrothermal Alteration, Geothermal, SAM, LSU

Abstract. The focus of this paper is to evaluate the performance of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for target detection of hydrothermal alteration zones associated with geothermal (GT) springs as proxy for narrowing areas of interest. The study employed the Per-pixel Spectral Angle Mapper (SAM) and the Sub-pixel Linear Spectral Unmixing (LSU) algorithms for spectral information extraction by using the ASTER satellite image data. In both cases, image endmember spectra specifically for kaolinite, alunite, and illite and calcite zones were selected and extracted by using the Analytical Imaging and Geophysics (AIG)-developed processing methods. The results of the analysis show that both SAM and LSU discriminated targets of interest better when employing image spectra and poorly when using library spectra. However, the Per-pixel SAM is unsuitable for target detection and more suited where the objective of the investigation is to classify whole scene and not particular targets as in this case. The LSU was found to be effective for discriminating alterations associated with the thermal springs especially where image endmember spectra are employed for analysis, thus recommended for prefeasibility mapping of GT related resources.