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)

Abstract

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.

Keywords

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

References

  1. 1.
    Avery, T.E., Berlin, G.L.: Fundamentals of Remote Sensing and Airphoto Interpretation. Macmillan Publishing Co., New York (1992)Google Scholar
  2. 2.
    Murai, H., Omatsu, S., OE, S.: Principal Component Analysis for Remotely Sensed Data Classified by Kohonen’s Feature Mapping Preprocessor and Multi-Layered Neural Network Classifier. IEICE Trans.Commun. E78-B(12), 1604–1610 (1995)Google Scholar
  3. 3.
    Zeng, X.-Y., Chen, Y.-W., Nakao, Z.: Classification of remotely sensed images using independent component analysis and spatial consistency. Journal of Advanced Computational Intelligence and Intelligent Informatics 8, 216–222 (2004)CrossRefGoogle Scholar
  4. 4.
    He, X., Niyogi, P.: Locality Preserving Projections. In: Advances in Neural Information Processing Systems, Vancouver, Canada, vol. 16 (2003)Google Scholar
  5. 5.
    Specht, D.F.: Enhancements to Probabilistic Neural Networks. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN 1992), vol. 1, pp. 761–768 (1992)Google Scholar
  6. 6.
    Chen, Y.-W., Han, X.-H.: Classification of High-Resolution Satellite Images Using Supervised Locality Preserving Projections. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 149–156. Springer, Heidelberg (2008)CrossRefGoogle Scholar

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

Personalised recommendations