Feature reduction of hyperspectral images: Discriminant analysis and the first principal component
Publish Year: 1394
نوع سند: مقاله ژورنالی
زبان: English
View: 361
This Paper With 9 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_JADM-3-1_001
تاریخ نمایه سازی: 19 تیر 1398
Abstract:
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.
Keywords:
Authors
Maryam Imani
Faculty of Electrical and Computer Engineering, Tarbiat Modares University
Hassan Ghassemian
Faculty of Electrical and Computer Engineering, Tarbiat Modares University