Spectral analysis methods were used for detection of mineralogical features on a set of Enhanced Thematic Mapper Plus data of Behabad zone, central Iran. Several indicative minerals for hydrothermal alterations were identified in the study area. The spectra of unknowns were determined by comparing to USGS mineral spectral library. Different pre-processings and processings were performed to achieve the highest possible accuracy. These are the minimum noise fraction, the pixel purity index analysis, spectral feature fitting, spectral angle mapper and binary encoding. The results of spectral analysis, as a map of minerals abundances, along with the results of other Image processing methods such as least square fit, and Crosta method were integrated within ArcGIS Software to achive a potential map of hydrothermal alterations. The minerals: allanite, magnetite, alunite, clay minerals, and muscovite were therefore detected and mapped in this study. The detected alterations here highly match to the mineral concentrations which are formerly found and measured in the study area that emphasizes the reliability of the applied method.

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We present a method for combined classification aiming to map alterations in a set of ASTER (Advanced Spaceborne Thermal Emission and reflection) data in the Erongo Complex, Namibia. Ten alterations detected by the matched filtering unmixing method on the Hyperion dataset of the area are therefore used as training classes. The separability of the classes was computed to evaluate the ability of ASTER data to spectrally discriminate between these classes. The outcome of this computation is satisfactory for the highprobability training dataset. In order to improve the accuracy of upcoming processes, classes with high similarity (low separability) were combined. The classification of ASTER scene is then performed with the use of both individual and combined classification classifiers. A new combined classification method (named selective combined classification (SCC)) was developed in this research to achieve the highest possible accuracy in the resultant classification map. An accuracy analysis has proven the advantages and capability of SCC among all classifiers tested in this study (both individual and combined).

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