23 October 2010 Integrated use of Hyperion and ASTER data for alteration mapping
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Abstract
Mapping of alterations in a geological terrain can be considered as a classification task in the remote sensing data processing. Training dataset is an important part of a classification process. Collecting of precise training data is generally expensive and time consuming. In this study, the alteration map resulted by Hyperion is used as training data for classification of the ASTER scene in Erongo, Namibia. This extends results to a much broader in comparison to Hyperion scene. Ten alterations detected by the matched filtering unmixing method on the Hyperion dataset are therefore training classes of the classification. 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 classifiers. An accuracy analysis was performed to compare the accuracy of each classifier. The Mahalanobis distance method has the best performance among all classifiers regarding to its highest overall accuracy.
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Majid M. Oskouei, Majid M. Oskouei, } "Integrated use of Hyperion and ASTER data for alteration mapping", Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 783106 (23 October 2010); doi: 10.1117/12.858602; https://doi.org/10.1117/12.858602
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