Exploitation
H. Aryanmehr; M. Hosseinjanizadeh; M. Honarmand; F. Naser
Abstract
In this work, we focus on investigating the Quickbird and Landsat-8 datasets for mapping hydrothermal and gossans alterations in reconnaissance porphyry copper mineralization in the Babbiduyeh area. This area is situated in the Central Iranian Volcano-sedimentary Complex, where large copper deposits ...
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In this work, we focus on investigating the Quickbird and Landsat-8 datasets for mapping hydrothermal and gossans alterations in reconnaissance porphyry copper mineralization in the Babbiduyeh area. This area is situated in the Central Iranian Volcano-sedimentary Complex, where large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The alteration zones are discriminated by implementation of band ratio and principal component analysis on the Quickbird and Landsat-8 datasets. The image processing results are evaluated by field surveys, X-ray diffraction (XRD), microscopic thin section, and spectroscopic studies of field samples as well as the 1:100000 Sarduiyeh and 1:5000 Babbiduyeh geological maps. In addition, the spectral characterizations of the samples are analyzed by visual inspection, and the PIMAView, SAMS, and ViewSpecpro software programs. The combined spectroscopic measurements, XRD analyses, and petrographic studies revealed mineral assemblages typical of the phyllic, phyllic-supergen, propylitic, argillic, and gossan alterations. The results obtained from image processing and analysis of field samples illustrated examples of effects of iron oxides and hydroxides on the surface of phyllic and argillic alterations. Hence, it can be concluded that the areas discriminated in Quickbird as gossans correspond to the phyllic and argillic alteration areas.
A. Salimi; M. Ziaii; A. Amiri; M. Hosseinjani Zadeh
Abstract
Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed ...
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Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called training samples and end-members. The performance of algorithms using training samples can be negatively affected by the curse of dimensionality. This problem is usually observed in the hyperspectral image classification, especially when a low number of training samples, compared to the large number of spectral bands of hyperspectral data, are available. An unmixing method that is not highly impressed by the curse of dimensionality is a promising option. Among all the methods used, Support Vector Machine (SVM) is a more robust algorithm used to overcome this problem. In this work, our aim is to evaluate the capability of a regression mode of SVM, namely Support Vector Regression (SVR), for the sub-pixel classification of alteration zones. As a case study, the Hyperion data for the Sarcheshmeh, Darrehzar, and Sereidun districts is used. The main classification steps rely on 20 field samples taken from the Darrehzar area divided into 12 and 8 samples for training and validation, respectively. The accuracy of the sub-pixel maps obtained demonstrate that SVR can be successfully applied in the curse of dimensional conditions, where the size of the training samples (12) is very low compared to the number of spectral bands (165).
