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Semi-supervised Hyperspectral Image Classification with Graphs

2006

Conference Paper

ei


This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the images through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.

Author(s): Bandos, TV. and Zhou, D. and Camps-Valls, G.
Book Title: IGARSS 2006
Journal: Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006)
Pages: 3883-3886
Year: 2006
Month: August
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IGARSS.2006.996
Event Name: IEEE International Conference on Geoscience and Remote Sensing
Event Place: Denver, CO, USA

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{3955,
  title = {Semi-supervised Hyperspectral Image
  Classification with Graphs},
  author = {Bandos, TV. and Zhou, D. and Camps-Valls, G.},
  journal = {Proceedings of the IEEE International Conference on Geoscience and Remote Sensing (IGARSS 2006)},
  booktitle = {IGARSS 2006},
  pages = {3883-3886},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = aug,
  year = {2006},
  month_numeric = {8}
}