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 saltaffected areas.
Keywords: remote sensing; digital earth; digital image classification; MLP neural network; lithological mapping; salt diapir; ASTER; Zagros; Iran; Geology; image processing
Mohammad H. Tayebi , Majid H. Tangestani & Hasan Roosta (2013): Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data, International Journal of Digital Earth, 6:2, 143-157