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Proceedings Paper

Semi-supervised classification for hyperspectral remote sensing image based on PCA and kernel FCM algorithm
Author(s): Xiaofang Liu; Binbin He; Xiaowen Li
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Paper Abstract

Hyperspectral remote sensing image classification is a challenging task in remote sensing applications because this image always has some information redundancy and is easy to be affected by noise or lack of the separability. A semi-supervised classification method based on principal component analysis (PCA) method and kernel fuzzy C-means (KFCM) algorithm for hyperspectral remote sensing image is proposed in this paper. First the PCA method finds an effective representation of spectral signature in a reduced dimensional feature space. Then a semi-supervised kernel-based FCM algorithm, called SSKFCM algorithm by introducing semi-supervised learning technique and the kernel trick simultaneously into conventional fuzzy C-means algorithm, is introduced to classify the feature vectors. Finally numerical experiments are conducted on a hyperspectral remote sensing image that provides digital images of 80 spectral bands with wavelength rang from 455 nm to 1642 nm. Classification performance is estimated by classification accuracy and kappa coefficient. The simulation results show that the proposed approach can be effectively applied to hyperspectral remote sensing image classification.

Paper Details

Date Published: 7 November 2008
PDF: 10 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71471I (7 November 2008); doi: 10.1117/12.813255
Show Author Affiliations
Xiaofang Liu, Univ. of Electronic Science and Technology of China (China)
Sichuan Univ. of Science and Engineering (China)
Binbin He, Univ. of Electronic Science and Technology of China (China)
Xiaowen Li, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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