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A Semi-Supervised Feature Extraction based on Supervised and Fuzzy-based Linear Discriminant Analysis for Hyperspectral Image Classification |
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PP: 81-87 |
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Author(s) |
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Cheng-Hsuan Li,
Hsin-Hua Ho,
Bor-Chen Kuo,
Jin-Shiuh Taur,
Hui-Shan Chu,
Min-Shian Wang,
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Abstract |
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| Linear discriminant analysis (LDA) is a commonly used feature extraction method to resolve the Hughes phenomenon for
classification. Moreover, many studies show that the spatial information can greatly improve the classification performance. Hence,
for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial
information. Recently, we proposed a fuzzy-based LDA (FLDA), an unsupervised feature extraction, and used it for clustering problem.
However, it is hard to apply in the image segmentation because the optimization problem is nonlinear and non-convex and the number
of membership values, the product of the number of clusters and the number of pixels in the image, is too large. In this paper, a semisupervised
feature extraction method which is based on the scatter matrices of LDA and FLDA (FLDA) is proposed. The unknown
samples and their membership values which are determined by the posteriors after applying the classifier are used to form the withinand
between-cluster scatter matrices of FLDA. The experimental results on two hyperspectral images, the Washington DC Mall and
the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size
problem. |
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