Land-cover classification using both hyperspectral and LiDAR data

Ghamisi, Pedram, Benediktsson, Jon Atli and Phinn, Stuart (2015) Land-cover classification using both hyperspectral and LiDAR data. International Journal of Image and Data Fusion, 6 3: 189-215. doi:10.1080/19479832.2015.1055833

Author Ghamisi, Pedram
Benediktsson, Jon Atli
Phinn, Stuart
Title Land-cover classification using both hyperspectral and LiDAR data
Journal name International Journal of Image and Data Fusion   Check publisher's open access policy
ISSN 1947-9824
Publication date 2015-06-13
Sub-type Article (original research)
DOI 10.1080/19479832.2015.1055833
Open Access Status
Volume 6
Issue 3
Start page 189
End page 215
Total pages 27
Place of publication Singapore, Singapore
Publisher Taylor & Francis Asia Pacific
Collection year 2016
Language eng
Abstract The increased availability of data from different satellite and airborne sensors from a particular scene makes it desirable to jointly use data from multiple data sources for improved information extraction and classification. In particular, hyperspectral sensors provide valuable spectral information that can be used to discriminate different classes of interest, but they do not provide structural and elevation information. On the other hand, LiDAR data can extract useful information related to the size, structure and elevation of different objects, but cannot model the spectral characteristics of different materials. In this paper, a new classification framework is proposed by considering the integration of hyperspectral and LiDAR data. In this case, the recently introduced theoretically sound attribute profile (AP) is considered to model the spatial information of LiDAR and hyperspectral data. In parallel, in order to reduce the redundancy of the hyperspectral data and address the so-called curse of dimensionality, supervised feature extraction techniques are taken into account. Then, the new features obtained by the AP and the supervised feature extraction techniques are concatenated into a stacked vector. The final classification map is achieved by using either support vector machine or random forest classification techniques. The proposed method was applied on two data sets and the obtained results were compared in terms of classification accuracies and CPU processing time. From the results it can be concluded that the proposed method can classify the integration of hyperspectral and LiDAR data accurately in a very acceptable CPU processing time. It should be noted that the proposed method is fully automatic and there is no need to set any parameters to increase the favourability of the proposed method.
Keyword Hyperspectral
Extended multi-attribute profile
Support vector machine classification
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Early view of article.

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
Official 2016 Collection
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 3 times in Scopus Article | Citations
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