Data-dependent semi-supervised hyperspectral image classification

Haobo Lv, Xiaoqiang Lu, Yuan Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Hyperspectral imagery provides more powerful information than multispectral remote sensing data. However, when hyperspectral data is used for classification task, the highdimension features often lead to ill-conditioned problems, such as the Hughes phenomenon. To tackle this problem, various supervised dimensional reduction methods are proposed. However, these methods only exploit the labeled training data and ignore the huge unlabelled data. To utilize the unlabelled data space structure information in dimension reduction, a method is proposed as Data-dependent semi-supervised (DDSS). The proposed method exploits the space structure of labeled data and unlabelled data jointly to reduce the dimensionality of the image cures. Experimental results show that this method significantly outperforms the state-of-the-art dimension reduction methods for classification and denoising.

Original languageEnglish
Title of host publication2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
Pages664-668
Number of pages5
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, China
Duration: 6 Jul 201310 Jul 2013

Publication series

Name2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

Conference

Conference2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
Country/TerritoryChina
CityBeijing
Period6/07/1310/07/13

Keywords

  • dimension reduction
  • Euclidean embedding
  • Hyperspectral image
  • Semi-supervised

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