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Boosted band ratio feature selection for hyperspectral image classification
conference contribution
posted on 2006-12-01, 00:00 authored by F Zhouyu, Terry CaelliTerry Caelli, L Nianjun, Antonio Robles-KellyAntonio Robles-KellyBand ratios have many useful applications in hyperspectral image analysis. While optimal ratios have been chosen empirically in previous research, we propose a principled algorithm for the automatic selection of ratios directly from data. First, a robust method is used to estimate the Kullback-Leibler divergence (KLD) between different sample distributions and evaluate the optimality of individual ratio features. Then, the boosting framework is adopted to select multiple ratio features iteratively. Multiclass classification is handled by using a pairwise classification framework. The algorithm can also be applied to the selection of discriminant bands. Experimental results on both simple material identification and complex land cover classification demonstrate the potential of this ratio selection algorithm. © 2006 IEEE.
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Volume
1Pagination
1059 - 1062Publisher DOI
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1051-4651ISBN-13
9780769525211ISBN-10
0769525210Publication classification
E1.1 Full written paper - refereedTitle of proceedings
Proceedings - International Conference on Pattern RecognitionUsage metrics
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