Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area,SE Kerman,Iran

This paper is an attempt to introduce the role of earth observation technology and a type of digital earth processing in mineral resources exploration and assessment.The sub-pixel distribution and quantity of alteration minerals were mapped using linear spectral unmixing(LSU)and mixture tuned matched filtering(MTMF)algorithms in the Sarduiyeh area,SE Kerman,Iran,using the visible-near infrared(VNIR)and short wave infrared(SWIR)bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)instrument and the results were compared to evaluate the efficiency of methods.Three groups of alteration minerals were identified:(1)pyrophylite-alunite(2)sericite-kaolinite,and(3)chlorite-calcite-epidote.Results showed that high abundances within pixels were successfully corresponded to the alteration zones.In addition,a number of unreported altered areas were identified.Field observations and X-ray diffraction(XRD)analysis of field samples confirmed the dominant mineral phases identified remotely.Results of LSU and MTMF were generally similar with overall accuracy of 82.9 and 90.24%,respectively.It is concluded that LSU and MTMF are suitable for sub-pixel mapping of alteration minerals and when the purpose is identification of particular targets,rather than all the elements in the scene,the MTMF algorithm could be proposed.

Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data

This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron(MLP)in the Zagros Folded Belt,Iran,and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact assessment.MLP neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data,and the results were compared using confusion matrices.The most appropriate classification image for L1B input to MLP was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%.The MLP result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs,respectively.Field observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified remotely.It is concluded that MLP is an efficient approach for mapping salt diapirs and salt-affected areas.

Evaluating pixel-based vs.object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite imagery.The study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous rocks.When performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)algorithm.However,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)method.Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively.Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of PBIA.OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders.This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery.It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.