Advanced search
1 file | 1.50 MB Add to list

Semi-supervised graph fusion of Hyperspectral and LiDAR Data for classification

Author
Organization
Project
  • FWO
Abstract
This paper proposes a semi-supervised graph-based fusion framework to couple dimensionality reduction and the fusion of multi-sensor data for classification. First, morphological features are used to model the elevation and spatial information contained in both LiDAR data and on the first few principal components (PCs) of the original hyperspectral (HS) image. Then, we fuse the features by projecting the spectral, spatial and elevation features onto a lower subspace through our proposed semi-supervised fusion graph. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or unsupervised graph fusion, with the proposed method, overall classification accuracies were improved by 9% and 4%, respectively.
Keywords
graph-based, Data fusion, remote sensing, hyperspectral image, LiDAR data, AREAS, IMAGES, FEATURE-EXTRACTION, DIRECTIONAL MORPHOLOGICAL PROFILES

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.50 MB

Citation

Please use this url to cite or link to this publication:

MLA
Liao, Wenzhi, et al. “Semi-Supervised Graph Fusion of Hyperspectral and LiDAR Data for Classification.” IEEE International Symposium on Geoscience and Remote Sensing IGARSS, edited by Vito Pascazio and Sebastiano B Serpico, IEEE, 2015, pp. 53–56.
APA
Liao, W., Xia, J., Du, P., & Philips, W. (2015). Semi-supervised graph fusion of Hyperspectral and LiDAR Data for classification. In V. Pascazio & S. B Serpico (Eds.), IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 53–56). Milan, Italy: IEEE.
Chicago author-date
Liao, Wenzhi, Junshi Xia, Peijun Du, and Wilfried Philips. 2015. “Semi-Supervised Graph Fusion of Hyperspectral and LiDAR Data for Classification.” In IEEE International Symposium on Geoscience and Remote Sensing IGARSS, edited by Vito Pascazio and Sebastiano B Serpico, 53–56. IEEE.
Chicago author-date (all authors)
Liao, Wenzhi, Junshi Xia, Peijun Du, and Wilfried Philips. 2015. “Semi-Supervised Graph Fusion of Hyperspectral and LiDAR Data for Classification.” In IEEE International Symposium on Geoscience and Remote Sensing IGARSS, ed by. Vito Pascazio and Sebastiano B Serpico, 53–56. IEEE.
Vancouver
1.
Liao W, Xia J, Du P, Philips W. Semi-supervised graph fusion of Hyperspectral and LiDAR Data for classification. In: Pascazio V, B Serpico S, editors. IEEE International Symposium on Geoscience and Remote Sensing IGARSS. IEEE; 2015. p. 53–6.
IEEE
[1]
W. Liao, J. Xia, P. Du, and W. Philips, “Semi-supervised graph fusion of Hyperspectral and LiDAR Data for classification,” in IEEE International Symposium on Geoscience and Remote Sensing IGARSS, Milan, Italy, 2015, pp. 53–56.
@inproceedings{6908425,
  abstract     = {{This paper proposes a semi-supervised graph-based fusion framework to couple dimensionality reduction and the fusion of multi-sensor data for classification. First, morphological features are used to model the elevation and spatial information contained in both LiDAR data and on the first few principal components (PCs) of the original hyperspectral (HS) image. Then, we fuse the features by projecting the spectral, spatial and elevation features onto a lower subspace through our proposed semi-supervised fusion graph. Experimental results on fusion of HS and LiDAR data from the 2013 IEEE GRSS Data Fusion Contest demonstrate effectiveness of the proposed method. Compared to the methods using single data source or unsupervised graph fusion, with the proposed method, overall classification accuracies were improved by 9% and 4%, respectively.}},
  author       = {{Liao, Wenzhi and Xia, Junshi and Du, Peijun and Philips, Wilfried}},
  booktitle    = {{IEEE International Symposium on Geoscience and Remote Sensing IGARSS}},
  editor       = {{Pascazio, Vito and B Serpico, Sebastiano}},
  isbn         = {{978-1-4799-7928-8}},
  issn         = {{2153-6996}},
  keywords     = {{graph-based,Data fusion,remote sensing,hyperspectral image,LiDAR data,AREAS,IMAGES,FEATURE-EXTRACTION,DIRECTIONAL MORPHOLOGICAL PROFILES}},
  language     = {{eng}},
  location     = {{Milan, Italy}},
  pages        = {{53--56}},
  publisher    = {{IEEE}},
  title        = {{Semi-supervised graph fusion of Hyperspectral and LiDAR Data for classification}},
  year         = {{2015}},
}

Web of Science
Times cited: