Feature extraction using Mel frequency cepstral coefficients for hyperspectral image classification
Applied Optics, Vol. 49, Issue 14, pp. 2670-2675 (2010)
http://dx.doi.org/10.1364/AO.49.002670
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Abstract
The Mel frequency cepstral coefficient (MFCC) model, which is widely used in speech detection and recognition, is introduced to extract features from hyperspectral image data. The similarities and differences between speech signals and spectral image data are compared and analyzed. The standard MFCC model is then improved to suit the characteristics of spectral image data by reintroducing the discarded phase information. Finally, the proposed model is applied to two real hyperspectral subimages. Experimental results show that the MFCC feature is sensitive and discriminative among reflectance spectra. It can be used as an effective feature extraction method for hyperspectral image classification.
© 2010 Optical Society of America
OCIS Codes
(070.4790) Fourier optics and signal processing : Spectrum analysis
(070.5010) Fourier optics and signal processing : Pattern recognition
(280.0280) Remote sensing and sensors : Remote sensing and sensors
ToC Category:
Fourier Optics and Signal Processing
History
Original Manuscript: November 24, 2009
Revised Manuscript: April 18, 2010
Manuscript Accepted: April 20, 2010
Published: May 6, 2010
Citation
Delian Liu, Xiaorui Wang, Jianqi Zhang, and Xi Huang, "Feature extraction using Mel frequency cepstral coefficients for hyperspectral image classification," Appl. Opt. 49, 2670-2675 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-14-2670
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