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Proceedings Paper

Adaptive locality preserving projection for hyperspectral image classification
Author(s): Lin He; Xianjun Chen; Xiaofeng Xie; Haokun Luo
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Paper Abstract

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relief the dilemma. Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143203 (14 February 2020); doi: 10.1117/12.2535915
Show Author Affiliations
Lin He, South China Univ. of Technology (China)
Xianjun Chen, South China Univ. of Technology (China)
Xiaofeng Xie, Hainan Univ. (China)
Haokun Luo, South China Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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