Accuracy assessment of hydrothermal mineral maps derived from ASTER images

F. Maghsoudi Moud, F.J.A. van Ruitenbeek, R.D. Hewson, M. van der Meijde, F. Deon

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The probability of a mineral exploration project failure has a direct relationship with the accuracy of output classified exploratory maps. In several geological remote sensing studies, computation of the accuracy of mineral maps has been done by either kappa coefficient or confusion matrix (e.g., Honarmand et al., 2016; Noori et al., 2019). However, the determination and quantification of inaccuracy sources have not been paid attention. The aim of this study is to know what are the inaccuracy sources which affect mineral maps derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images? Also, how much is the inaccuracy of each source? To find out the inaccuracy sources of mineral maps derived from ASTER images and quantify them, laboratory-based measurements were applied on collected outcrop rock samples from the study area. The laboratory-based measurements include SPECIM hyperspectral imaging and X-ray diffraction (XRD). The SPECIM hyperspectral camera records the short wave infrared (SWIR) range from 940 nm to 2540 nm with the 6 nm spectral resolution and 0.2 mm by 0.2 mm spatial resolution.
The Kuh Panj porphyry is a copper occurrence within the central part of the Kerman Cenozoic-Magmatic Arc, Iran. In this area, potassic, argillic, phyllic, propylitic, and silicification alteration zones are spread over an area of about 4 km2 (Nedimovic, 1973). The existence of different hydrothermal alteration zones and low vegetation coverage, make the area suitable for geological remote sensing studies and mineral mapping in particular.
Twenty weathered-altered outcrop rock samples from different parts of the Kuh Panj area were collected. The hyperspectral images of the rock samples were recorded to create a mineral map for each rock by using the spectral angle mapper (SAM) method. To obtain the optimum threshold value for the mineral map by the SAM method, the real value-area fractal method was applied on rule images of SAM (Shahriari et al., 2014). The threshold values affect the extension of borders of classes and as a result, the accuracy of the classified mineral map. Random pixel spectra were chosen from each rock to compute the accuracy of mineral maps by using the confusion matrix method. Afterward, the rock samples were powdered, and the rock powders were used for the XRD. The aim of using the XRD was to verify the hyperspectral mineral maps and identify non-SWIR active mineral composition of each sample. The same processing procedure, as applied on the SPECIM images, was used for the ASTER images (surface reflectance-VNIR and crosstalk corrected SWIR, AST_07XT, product) to map minerals. The accuracy of the mineral map derived from ASTER images was computed with the confusion matrix by using the rock sample information. Finally, the results of the SPECIM spectra were compared with the ASTER spectra and an error budget created to demonstrate the inaccuracy sources and their inaccuracy values. The inaccuracy sources include mismatching of the SPECIM spectra (15.21%) with the USGS spectral library, the SAM inaccuracy for the SPECIM mineral maps (14%), low reflectance of band five AST_07XT (24.5%), mismatching of the AST_07XT spectra (10%) with the resampled to ASTER USGS spectral library, spatial inaccuracy of AST_07XT (±29.4 m) in compare to AST_L1T (registered radiance and the sensor-precision terrain corrected), SAM inaccuracy for the AST_07XT mineral maps (12%), and human error (10.52%).
Original languageEnglish
Number of pages1
Publication statusPublished - 10 Dec 2019
Event30th Anniversary Conference of the Geological Remote Sensing Group (GRSG) 2019 - Frascati, Italy
Duration: 9 Dec 201912 Dec 2019
Conference number: 30


Conference30th Anniversary Conference of the Geological Remote Sensing Group (GRSG) 2019
Abbreviated titleGRSG 2019
Internet address


  • Accuracy assessment
  • Hydrothermal alteration
  • Laboratory experiments
  • XRD
  • Hyperspectral images
  • Kappa statistic
  • Spectral analysis
  • Mineral mapping
  • Copper
  • Porphyry


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