NDLI: Fusion of hyperspectral and LiDAR data in classification of urban areas

Content Provider	IEEE Xplore Digital Library
Author	Ghamisi, P. Benediktsson, J.A. Phinn, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Geogr. Sci. & Planning, Biophys. Remote Sensing Group, Univ. of Queensland, St. Lucia, QLD, Australia (Phinn, S.) \|\| Fac. of Electr. & Comput. Eng., Univ. of Iceland, Reykjavik, Iceland (Ghamisi, P.; Benediktsson, J.A.)
Abstract	In this paper, the fusion of hyperspectral and Li-DAR data is taken into account in order to develop a new classification framework for the accurate analysis of urban areas. In this method, an attribute profile is considered in order to model the spatial information of LiDAR and hyper-spectral data. In parallel, in order to reduce the redundancy of the hyperspectral data and address the so-called curse of dimensionality, a supervised feature extraction technique is used. Then, the new features obtained by the attribute profile and the supervised feature extraction technique are concatenated into a stacked vector. The final classification map is achieved by using a Random Forest classifier. Results infer that the proposed method can provide very good results in terms of classification accuracy and CPU processing time in an automatic manner.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	181
Ending Page	184
File Size	748298
Page Count	4
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6946386
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Laser radar Radio frequency Accuracy Standards supervised feature extraction LiDAR hyperspectral data fusion random forest
Content Type	Text
Resource Type	Article

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