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 conferencePaper

7 Citations (Scopus)

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.

Conference

ConferenceInternational Geoscience and Remote Sensing Symposium 2015
Abbreviated titleIGARSS 2015
CountryItaly
CityMilan
Period26/07/1531/07/15

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fusion
Data fusion
Electric fuses
Sensors
fuses
multisensor fusion
sensor
sensors
method

Keywords

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

Cite this

Liao, W., Xia, J., Du, P., Philips, W., Pascazio, V. (Ed.), & B Serpico, S. (Ed.) (2015). Semi-supervised graph fusion of hyperspectral and LiDAR data for classification. 53-56. Paper presented at International Geoscience and Remote Sensing Symposium 2015, Milan, Italy. https://doi.org/10.1109/IGARSS.2015.7325695
Liao, Wenzhi ; Xia, Junshi ; Du, Peijun ; Philips, Wilfried ; Pascazio, Vito (Editor) ; B Serpico, Sebastiano (Editor). / Semi-supervised graph fusion of hyperspectral and LiDAR data for classification. Paper presented at International Geoscience and Remote Sensing Symposium 2015, Milan, Italy.4 p.
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title = "Semi-supervised graph fusion of hyperspectral and LiDAR data for classification",
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",
author = "Wenzhi Liao and Junshi Xia and Peijun Du and Wilfried Philips and Vito Pascazio and {B Serpico}, Sebastiano",
year = "2015",
month = "11",
day = "12",
doi = "10.1109/IGARSS.2015.7325695",
language = "English",
pages = "53--56",
note = "International Geoscience and Remote Sensing Symposium 2015, IGARSS 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

}

Liao, W, Xia, J, Du, P, Philips, W, Pascazio, V (ed.) & B Serpico, S (ed.) 2015, 'Semi-supervised graph fusion of hyperspectral and LiDAR data for classification' Paper presented at International Geoscience and Remote Sensing Symposium 2015, Milan, Italy, 26/07/15 - 31/07/15, pp. 53-56. https://doi.org/10.1109/IGARSS.2015.7325695

Semi-supervised graph fusion of hyperspectral and LiDAR data for classification. / Liao, Wenzhi; Xia, Junshi; Du, Peijun; Philips, Wilfried; Pascazio, Vito (Editor); B Serpico, Sebastiano (Editor).

2015. 53-56 Paper presented at International Geoscience and Remote Sensing Symposium 2015, Milan, Italy.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Semi-supervised graph fusion of hyperspectral and LiDAR data for classification

AU - Liao, Wenzhi

AU - Xia, Junshi

AU - Du, Peijun

AU - Philips, Wilfried

A2 - Pascazio, Vito

A2 - B Serpico, Sebastiano

PY - 2015/11/12

Y1 - 2015/11/12

N2 - 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.

AB - 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.

KW - graph-based

KW - data fusion

KW - remote sensing

KW - hyperspectral image

KW - LiDAR data

KW - areas

KW - images

KW - feature extraction

KW - directional morphological profiles

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Liao W, Xia J, Du P, Philips W, Pascazio V, (ed.), B Serpico S, (ed.). Semi-supervised graph fusion of hyperspectral and LiDAR data for classification. 2015. Paper presented at International Geoscience and Remote Sensing Symposium 2015, Milan, Italy. https://doi.org/10.1109/IGARSS.2015.7325695