سیویلیکا را در شبکه های اجتماعی دنبال نمایید.

Feature reduction of hyperspectral images: Discriminant analysis and the first principal component

سال انتشار: 1394
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 455

فایل این مقاله در 9 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

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