Semi-supervised graph fusion of hyperspectral and LiDAR data for classification

Wenzhi Liao, Junshi Xia, Peijun Du, Wilfried Philips, Vito Pascazio (Editor), Sebastiano B Serpico (Editor)

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 12 Nov 2015
EventIEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015) - Convention Center, Milan, Italy
Duration: 26 Jul 201531 Jul 2015


ConferenceIEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)
Abbreviated titleIGARSS 2015


  • graph-based
  • data fusion
  • remote sensing
  • hyperspectral image
  • LiDAR data
  • areas
  • images
  • feature extraction
  • directional morphological profiles


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