Dimensionally Reduced Features for Hyperspectral Image Classification Using Deep Learning

  • K. S. CharmishaEmail author
  • V. Sowmya
  • K. P. Soman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 500)


Hyperspectral images (HSIs) cover a wide range of spectral bands in the electromagnetic spectrum with a very finite interval, and with high spectral resolution of data. The main challenges encountered with HSIs are those associated with their large dimensions. To overcome these challenges we need a healthy classification technique, and we need to be able to extract required features. This chapter analyzes the effect of dimensionality reduction on vectorized convolution neural networks (VCNNs) for HSI classification. A VCNN is a recently introduced deep-learning architecture for HSI classification. To analyze the effect of dimensionality reduction (DR) on VCNN, the network is trained with dimensionally reduced hyperspectral data. The network is tuned in accordance with the learning rate and number of iterations. The effect of a VCNN is analyzed by computing overall accuracy, classification accuracy, and the total number of trainable parameters required before and after DR. The reduction technique used is dynamic mode decomposition (DMD), which is capable of selecting most informative bands using the concept of eigenvalues. Through this DR technique for HSI classification using a VCNN, comparable classification accuracy is obtained using the reduced feature dimension and a lesser number of VCNN trainable parameters.


Hyperspectral images Dimensionality reduction Convolution neural network Dynamic mode decomposition Trainable parameters Learning rate 


  1. 1.
    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS et al (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv: 603.04467
  2. 2.
    Aswathy C, Sowmya V, Soman KP (2015) ADMM based hyperspectral image classification improved by denoising using Legendre Fenchel transformation. Ind J Sci Technol 8(24):1Google Scholar
  3. 3.
    Chen, Y, Jiang H, Li C, Jia X, Ghamisi P (2016) Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sens 54(10):6232–6251Google Scholar
  4. 4.
    Deepa Merlin Dixon K, Sowmya V, Soman KP (2017) Effect of denoising on vectorized convolutional neural network for hyperspectral image classification. In: International conference on Nextgen electronic technologies: silicon to software (ICNETS2). LNEE Springer proceedingsGoogle Scholar
  5. 5.
    Hu W, Huang Y, Wei L, Zhang L, Li H (2015) Deep convolutional neural networks for hyperspectral image classification. J Sens 2015Google Scholar
  6. 6.
    Koonsanit K, Jaruskulchai C, Eiumnoh A (2012) Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique. Int J Mach Learn Comput 2(3):248Google Scholar
  7. 7.
    Megha P, Sowmya V, Soman KP (2017) Effect of dynamic mode decomposition based dimension reduction technique on hyperspectral image classification. In: International conference on Nextgen electronic technologies: silicon to software (ICNETS2). LNEE Springer proceedingsGoogle Scholar
  8. 8.
    Sadek RA (2012) SVD based image processing applications: state of the art, contributions and research challenges. arXiv: 1211.7102
  9. 9.
    Schmid Peter J (2010) Dynamic mode decomposition of numerical and experimental data. J Fluid Mech 656:5–28MathSciNetCrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Centre for Computational Engineering and NetworkingAmrita School of Engineering, Amrita Vishwa VidyapeetamCoimbatoreIndia

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