Feature reduction of hyperspectral images: Discriminant analysis and the first principal component | ||
| Journal of AI and Data Mining | ||
| مقاله 1، دوره 3، شماره 1، خرداد 2015، صفحه 1-9 اصل مقاله (783.72 K) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.5829/idosi.JAIDM.2015.03.01.01 | ||
| نویسندگان | ||
| Maryam Imani؛ Hassan Ghassemian* | ||
| Faculty of Electrical and Computer Engineering, Tarbiat Modares University | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Discriminant analysis؛ Principal component؛ Feature reduction؛ Hyperspectral؛ Classification | ||
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