Feature extraction using Mel frequency cepstral coefficients for hyperspectral image classification

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.