Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
عنوان مقاله: Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
شناسه ملی مقاله: JR_JADM-3-1_001
منتشر شده در شماره 1 دوره 3 فصل در سال 1394
شناسه ملی مقاله: JR_JADM-3-1_001
منتشر شده در شماره 1 دوره 3 فصل در سال 1394
مشخصات نویسندگان مقاله:
Maryam Imani - Faculty of Electrical and Computer Engineering, Tarbiat Modares University
Hassan Ghassemian - Faculty of Electrical and Computer Engineering, Tarbiat Modares University
خلاصه مقاله:
Maryam Imani - Faculty of Electrical and Computer Engineering, Tarbiat Modares University
Hassan Ghassemian - Faculty of Electrical and Computer Engineering, Tarbiat Modares University
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.
کلمات کلیدی: Discriminant analysis, Principal component, Feature reduction, Hyperspectral, Classification
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/894187/