NDLI: Spectral and spatial classification of hyperspectral data using SVMs and Gabor textures

Content Provider	IEEE Xplore Digital Library
Author	Lian-Zhi Huo Ping Tang
Copyright Year	2011
Description	Author affiliation: Institute of Remote Sensing Applications, Chinese Academy of Sciences, Chao Yang, Beijing 100101, China (Lian-Zhi Huo; Ping Tang)
Abstract	High spectral and spatial resolution images are widely used in urban mapping. In order to increase conventional spectral classification accuracy, morphological profiles based spatial information were widely studied. Some improvements were obtained in terms of classification accuracy. However, texture is is a very useful structure information in high spatial resolution images. In this paper, Gabor filters based textures are investigated to increase conventional spectral classification. Gabor textures are extracted from the first three PCs of the hyperspectral bands. Then Gabor textures and hyperspectral bands are concatenated, and classified in one support vector machines. The results show that Gabor textures are useful in extracting spatial information and increasing the classification accuracy compared to conventional spectral classification.
Starting Page	1708
Ending Page	1711
File Size	367087
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049564
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Hyperspectral imaging Accuracy Spatial resolution Educational institutions support vector machines Gabor texture hyperspectral
Content Type	Text
Resource Type	Article

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