Hyperspectral Image Classification Using Dimensionality Reduction Deep Networks

Authors

  • D. Kavitha Associate Professor, Department of Information Technology, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India.
  • S. Shobana Assistant Professor, R.M.K Engineering College, R.S.M Nagar, Kavaraipettai, Tamil Nadu, India.
  • S. Rajkumar Associate Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
  • Ganta Raghotham Reddy Professor, Department of ECE, Kakatiya Institute of technology and science, Warangal, Telangana, India
  • Ramesh Babu P. ssociate Professor, Department of Computer Science, College of Engineering and Technology, Wollega University, Nekemte, Oromia Region, Ethiopia.
  • Papiya Mukherjee Assistant Professor, Koneru Lakshmaiah Education Foundation, Department of CSE, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India

Keywords:

Convolutional neural network, image classification, hyperspectral imaging, dimensionality reduction

Abstract

In this research, we apply a convolutional neural network (CNN) to three publicly available hyperspectral datasets to determine which of these four models is the most effective when it comes to reducing the number of dimensions. The findings demonstrate that the models have a higher rate of classification accuracy on the smaller datasets when compared to the other techniques. It would appear from the observations that employing SuperPCA results in an overall improvement in the classifier level of effectiveness.

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References

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Published

05.12.2023

How to Cite

Kavitha, D. ., Shobana, S. ., Rajkumar, S. ., Reddy, G. R. ., Babu P., R. ., & Mukherjee, P. . (2023). Hyperspectral Image Classification Using Dimensionality Reduction Deep Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 81–86. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4035

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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