Typical sequence classification method in hyperspectral images with reduced bands

This work presents a new method for hyperspectral spectra classification based on the Typical Sequence (TS) derived from the Asymptotic Equipartition Theorem and Information Theory. Each Endmember (EM) of a scene is represented by a Hidden Markov Model (HMM) and a spectrum is classified in a given class if it can be considered a TS generated by the HMM associated with the EM related to the class. The Discrete Wavelet Transform (DWT) is used in the orthogonal decomposition of the original spectrum and the HMM parameters are estimated using this orthogonal decomposition. The proposed method is tested with AVIRIS spectra of a scene with 13 EM and the classification results show that 32 spectral bands can be used instead of the original 209 bands, without significant loss in the classification process.

[1]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[2]  Alex Bateman,et al.  An introduction to hidden Markov models. , 2007, Current protocols in bioinformatics.

[3]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[4]  Qian Du,et al.  Hidden Markov model approaches to hyperspectral image classification , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[5]  Cao Senmao,et al.  Application of semi-supervised dimensionality reduction for hyperspectral image classification , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[6]  Ahmed Hammouch,et al.  Hyperspectral images classification and Dimensionality Reduction using Homogeneity feature and mutual information , 2015, 2015 Intelligent Systems and Computer Vision (ISCV).

[7]  Shraddha P. Lodha,et al.  Dimensionality Reduction Techniques for Hyperspectral Images , 2014 .

[8]  Chein-I. Chang.  Spectral information divergence for hyperspectral image analysis , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[9]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[10]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[11]  Yuhong Yang,et al.  Information Theory, Inference, and Learning Algorithms , 2005 .

[12]  Hans-Georg Stark.  Wavelets and Signal Processing: An Application-Based Introduction , 2005 .

[13]  Qian Du,et al.  Hidden Markov model approach to spectral analysis for hyperspectral imagery , 2001 .