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Dimensionality Reduction by Dynamic Mode Decomposition for Hyperspectral Image Classification Using Deep Learning and Kernel Methods

  • K. S. Charmisha
  • V. Sowmya
  • K. P. Soman
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)

Abstract

Hyperspectral images are remotely sensed high dimension images, which capture a scene at different spectral wavelengths. There is a high correlation between the bands of these images. For an efficient classification and processing, the high data volume of the images need to be reduced. This paper analyzes the effect of dimensionality reduction on hyperspectral image classification using vectorized convolution neural network (VCNN), Grand Unified Recursive Least Squares (GURLS) and Support Vector Machines (SVM). Inorder to analyze the effect of dimensionality reduction, the network is trained with dimensionally reduced hyperspectral data for VCNN, GURLS and SVM. The experimental results shows that, one-sixth of the total number of available bands are the maximum possible reduction in feature dimension for Salinas-A and one-third of the total available bands are for Indianpines dataset that results in comparable classification accuracy.

Keywords

GURLS SVM Hyperspectral image classification Dimensionality reduction Dynamic mode decomposition Libsvm Gurls Classification accuracies 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Center for Computational Engineering and Networking (CEN)Amrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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