Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

Wenzhi Liao, Rik Bellens, Aleksandra Pizurica, Sidharta Gautama, Wilfried Philips, Monique Bernier, Josée Lévesque, Jean-Marc Garneau, Ellsworth LeDrew

Research output: Contribution to conferencePaperpeer-review

24 Citations (Scopus)


This paper proposes a method to combine feature fusion and decision fusion together for multi-sensor data classification. First, morphological features which contain elevation and spatial information, are generated on both LiDAR data and the first few principal components (PCs) of original hyperspectral (HS) image. We got the fused features by projecting the spectral (original HS image), spatial and elevation features onto a lower subspace through a graph-based feature fusion method. Then, we got four classification maps by using spectral features, spatial features, elevation features and the graph fused features individually as input of SVM classifier. The final classification map was obtained by fusing the four classification maps through the weighted majority voting. 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 only feature fusion, with the proposed method, overall classification accuracies were improved by 10% and 2%, respectively.
Original languageEnglish
Number of pages4
Publication statusPublished - 6 Nov 2014
Event 2014 IEEE Geoscience and Remote Sensing Symposium IGARSS - Quebec City, Canada
Duration: 13 Jul 201418 Jul 2014


Conference 2014 IEEE Geoscience and Remote Sensing Symposium IGARSS
Abbreviated titleIGARSS 2014
CityQuebec City


  • remote sensing
  • hyperspectral image
  • data fusion
  • areas
  • profiles
  • LiDAR data
  • graph-based
  • laser radar
  • data integration
  • geophysical image processing
  • image classification

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