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

Highly discriminative and adaptive feature extraction method based on NMF–MFCC for event recognition of Φ-OTDR

Yi Huang, Jingyi Dai, Wei Shen, Xiaofeng Chen, Chengyong Hu, Chuanlu Deng, Lin Chen, Xiaobei Zhang, Wei Jin, Jianming Tang, and Tingyun Wang
Appl. Opt. 62(35) 9326-9333 (2023)

Signal recognition method based on Mel frequency cepstral coefficients and fast dynamic time warping for optical fiber perimeter defense systems

Hainan Lu, Ningning Wang, Nian Fang, and Lutang Wang
Appl. Opt. 61(7) 1758-1765 (2022)

Tongue fissure extraction and classification using hyperspectral imaging technology

Qingli Li, Yiting Wang, Hongying Liu, Zhen Sun, and Zhi Liu
Appl. Opt. 49(11) 2006-2013 (2010)

Feature reduction and morphological processing for hyperspectral image data

David Casasent and Xue-Wen Chen
Appl. Opt. 43(2) 227-236 (2004)

Feature weighting algorithms for classification of hyperspectral images using a support vector machine

Bin Qi, Chunhui Zhao, and Guisheng Yin
Appl. Opt. 53(13) 2839-2846 (2014)