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Spatial–spectral method for classification of hyperspectral images |
Optics Letters, Vol. 38, Issue 6, pp. 815-817 (2013)
http://dx.doi.org/10.1364/OL.38.000815
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Abstract
Spatial–spectral approach with spatially adaptive classification of hyperspectral images is proposed. The rotation-invariant spatial texture information for each object is exploited and incorporated into the classifier by using the modified local Gabor binary pattern to distinguish different types of classes of interest. The proposed method can effectively suppress anisotropic texture in spatially separate classes as well as improve the discrimination among classes. Moreover, it becomes more robust with the within-class variation. Experimental results on the classification of three real hyperspectral remote sensing images demonstrate the effectiveness of the proposed approach.
© 2013 Optical Society of America
OCIS Codes
(100.5760) Image processing : Rotation-invariant pattern recognition
(330.6100) Vision, color, and visual optics : Spatial discrimination
(330.6180) Vision, color, and visual optics : Spectral discrimination
(100.3008) Image processing : Image recognition, algorithms and filters
(100.4145) Image processing : Motion, hyperspectral image processing
ToC Category:
Image Processing
History
Original Manuscript: October 16, 2012
Revised Manuscript: January 27, 2013
Manuscript Accepted: January 28, 2013
Published: March 5, 2013
Virtual Issues
Vol. 8, Iss. 4 Virtual Journal for Biomedical Optics
Citation
Xiaoyong Bian, Tianxu Zhang, Luxin Yan, Xiaolong Zhang, Houzhang Fang, and Hai Liu, "Spatial–spectral method for classification of hyperspectral images," Opt. Lett. 38, 815-817 (2013)
http://www.opticsinfobase.org/ol/abstract.cfm?URI=ol-38-6-815
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