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Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
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نویسنده
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Imani M. ,Ghassemian H.
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منبع
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journal of ai and data mining - 2015 - دوره : 3 - شماره : 1 - صفحه:1 -9
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چکیده
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When the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has not the limitation of linear discriminant analysis (lda) in the number of extracted features. in da-pc1, the dominant structure of distribution is preserved by pc1 and the class separability is increased by da. the experimental results show the good performance of da-pc1 compared to some state-of-the-art feature extraction methods.
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کلیدواژه
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Discriminant Analysis ,Principal Component ,Feature Reduction ,Hyperspectral ,Classification
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آدرس
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tarbiat modares university, Faculty of Electrical & Computer Engineering, ایران, tarbiat modares university, Faculty of Electrical & Computer Engineering, ایران
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Authors
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