Supervised Local Subspace Learning for Region Segmentation and Categorization in High-Resolution Satellite Images

  • Yen-wei Chen
  • Xian-hua Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5646)


We proposed a new feature extraction method based on supervised locality preserving projections (SLPP) for region segmentation and categorization in high-resolution satellite images. Compared with other subspace methods such as PCA and ICA, SLPP can preserve local geometric structure of data and enhance within-class local information. The generalization of the proposed SLPP based method is discussed in this paper.


supervised locality preserving projections region segmentation categorization high-resolution satellite images subspace learning independent component analysis generalization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yen-wei Chen
    • 1
    • 2
  • Xian-hua Han
    • 1
    • 2
  1. 1.Elect & Inf. Eng. SchoolCentral South Univ. of Forest and Tech.ChangshaChina
  2. 2.Graduate School of Science and EngineeringRitsumeikan UniversityJapan

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