A selective combined classification algorithm for mapping alterations on ASTER data

Abstract

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 high-probability 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).