Advertisement

Variational Mode Feature-Based Hyperspectral Image Classification

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78 %, average accuracy from 85.03 to 89.53 % while using 40 % data pixels for training.

Keywords

Hyperspectral imaging Variational mode decomposition Classification Orthogonal matching pursuit 

References

  1. 1.
    Kavitha, B., Sowmya, V., Soman, K.: Spatial preprocessing for improved sparsity based hyperspectral image classification. In: International Journal of Engineering Research and Technology, vol. 1, no. 5. 1em plus 0.5em minus 0.4em, ESRSA Publications (2012)Google Scholar
  2. 2.
    Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)CrossRefGoogle Scholar
  3. 3.
    Song, B., Li, J., Dalla Mura, M., Li, P., Plaza, A., Bioucas-Dias, J.M., Atli Benediktsson, J., Chanussot, J.: Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE Trans. Geosci. Remote Sens. 52(8), 5122–5136 (2014)CrossRefGoogle Scholar
  4. 4.
    Bhushan, D.B., Sowmya, V., Manikandan, M.S., Soman, K.: An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery. In: International Symposium on Ocean Electronics (SYMPOL), pp. 34–39. 1em plus 0.5em minus 0.4em, IEEE, 2011Google Scholar
  5. 5.
    Cai, T.T., Wang, L.: Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680–4688 (2011)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531–544 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Dragomiretskiy, K., Zosso, D.: Two-dimensional variational mode decomposition. In: Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 197–208. 1em plus 0.5em minus 0.4em, Springer (2015)Google Scholar
  8. 8.
    Suchithra, M., Sukanya, P., Prabha, P., Sikha, O., Sowmya, V., Soman, K.: An experimental study on application of orthogonal matching pursuit algorithm for image denoising. In: International Multi-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 729–736. 1em plus 0.5em minus 0.4em, IEEE (2013)Google Scholar
  9. 9.
    Vidal, M., Amigo, J.M.: Pre-processing of hyperspectral images essential steps before image analysis. Chemometr. Intell. Lab. Syst. 117, 138–148 (2012)CrossRefGoogle Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Center for Excellence in Computational Engineering and NetworkingAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations