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http://dx.doi.org/10.7780/kjrs.2014.30.2.5

Spectral Mixture Analysis Using Modified IEA Algorithm for Forest Classification  

Song, Ahram (Department of Civil and Environmental Engineering, Seoul National University)
Han, Youkyung (Department of Civil and Environmental Engineering, Seoul National University)
Kim, Younghyun (Department of Civil and Environmental Engineering, Seoul National University)
Kim, Yongil (Department of Civil and Environmental Engineering, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.30, no.2, 2014 , pp. 219-226 More about this Journal
Abstract
Fractional values resulted from the spectral mixture analysis could be used to classify not only urban area with various materials but also forest area in more detailed spatial scale. Especially South Korea is largely consist of mixed forest, so the spectral mixture analysis is suitable as a classification method. For the successful classification using spectral mixture analysis, extraction of optimal endmembers is prerequisite process. Though geometric endmember selection has been widely used, it is barely suitable for forest area. Therefore, in this study, we modified Iterative Error Analysis (IEA), one of the most famous algorithms of image endmember selection which extracts pure pixel directly from the image. The endmembers which represent deciduous and coniferous trees are automatically extracted. The experiments were implemented on two sites of Compact Airborne Spectrographic Imager (CASI) and classified forest area into two types. Accuracies of each classification results were 86% and 90%, which mean proposed algorithm effectively extracted proper endmembers. For the more accurate classification, another substances like forest gap should be considered.
Keywords
Forest classification; Spectral unmixing; IEA; Endmember;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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