Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 3، شماره: 1
سال انتشار: 1394
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 455
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شناسه ملی سند علمی:
JR_JADM-3-1_001
تاریخ نمایه سازی: 19 تیر 1398
چکیده مقاله Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
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.
کلیدواژه های Feature reduction of hyperspectral images: Discriminant analysis and the first principal component:
نویسندگان مقاله Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
Maryam Imani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University
Hassan Ghassemian
Faculty of Electrical and Computer Engineering, Tarbiat Modares University