Virtual Dimensionality estimation by Double Subspace Projection for hyperspectral images

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2 Scopus citations

Abstract

Virtual Dimensionality (VD) estimation is a key problem in feature/band selection and spectral mixture analysis of hyperspectral images. In this paper, a Double Subspace Projection (DSubP) based VD estimation algorithm is proposed. The pixel representation and image representation of a hyperspectral image are utilized to generate two subspaces according to the principal component analysis (PCA), respectively. When the dimensionality of these two subspaces exceeds VD of the hyperspectral image, both sub-space projections show the same reconstruction performance. Therefore, VD can be estimated by judging the difference of reconstruction performance between DSubP. Both synthetic and real hyperspectral experiments demonstrate that the performance of the proposed DSubP based VD estimation algorithm outperforms that of the HFC and NWHFC based VD estimation algorithms.

Original languageEnglish
Title of host publication2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Pages234-237
Number of pages4
DOIs
StatePublished - 2010
Event2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010 - Qingdao, China
Duration: 28 Aug 201031 Aug 2010

Publication series

Name2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Volume2

Conference

Conference2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Country/TerritoryChina
CityQingdao
Period28/08/1031/08/10

Keywords

  • Band selection
  • Feature extraction
  • Intrinsic dimensionality
  • Spectral mixture analysis
  • Virtual Dimensionality

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