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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)

Abstract

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.

Keywords

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

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Copyright information

© 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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