An advanced classifier for the joint use of LiDAR and hyperspectral data: case study in Queensland, Australia

Ghamisi, P., Wu, D., Cavallaro, G., Benediktsson, J. A., Phinn, S. and Falco, Nicola (2015). An advanced classifier for the joint use of LiDAR and hyperspectral data: case study in Queensland, Australia. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, (2354-2357). 26-31 July 2015. doi:10.1109/IGARSS.2015.7326281


Author Ghamisi, P.
Wu, D.
Cavallaro, G.
Benediktsson, J. A.
Phinn, S.
Falco, Nicola
Title of paper An advanced classifier for the joint use of LiDAR and hyperspectral data: case study in Queensland, Australia
Conference name IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Conference location Milan, Italy
Conference dates 26-31 July 2015
Convener IEEE
Proceedings title 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Journal name 2015 Ieee International Geoscience and Remote Sensing Symposium (Igarss)
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/IGARSS.2015.7326281
Open Access Status Not Open Access
ISBN 9781479979295
ISSN 2153-6996
Volume 2015-November
Start page 2354
End page 2357
Total pages 4
Language eng
Abstract/Summary With respect to the exponential increase in the number of available remote sensors in recent years, the possibility of having different types of data captured over the same scene, has resulted in many research works related to the joint use of passive and active sensors for the accurate classification of different materials. However, until now, there is a small number of research works related to the integration of highly valuable information obtained from the joint use of LiDAR and hyperspectral data. This paper proposes an efficient classification approach in terms of accuracies and demanded CPU processing time for integrating big data sets (e.g., LiDAR and hyperspectral) to provide land cover mapping capabilities at a range of spatial scales. In addition, the proposed approach is fully automatic and is able to efficiently handle big data containing a huge number of features with very limited number of training samples in few seconds.
Keyword Fusion of LiDAR and hyperspectral
Big data processing
Self-dual attribute profile
Random forest classifier
Attribute Profiles
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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