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Hyperspectral Image Features Classification Using Deep Learning Recurrent Neural Networks.

R Venkatesan1, S Prabu2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. venkatesaneng@gmail.com.

Journal of Medical Systems
|June 6, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Recurrent Neural Network (RNN) for hyper-spectral image classification, analyzing pixels as sequential data. The proposed DL-RNN model improves classification accuracy over traditional deep learning methods.

Keywords:
Activation functionsDeep learningFeatures vectorsHyperspectral imagingRecurrent neural network

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Deep learning (DL) techniques, including object detection and classification, have shown significant success in remote sensing.
  • Analyzing hyper-spectral data requires models that can process pixel information as sequences.

Purpose of the Study:

  • To introduce a novel framework using Recurrent Neural Network (RNN) for hyper-spectral image classification.
  • To propose an activation function and parameter rectification functions for analyzing sequential data in hyper-spectral images.

Main Methods:

  • Implementation of a Deep Learning framework utilizing Recurrent Neural Network (RNN) on hyper-spectral data.
  • Processing hyper-spectral image pixels from a sequential perspective to capture sequence-based data.
  • Development of a novel activation function and parameter rectification functions within the DL-RNN model.

Main Results:

  • The proposed DL-RNN model effectively analyzes hyper-spectral pixels as sequences.
  • The activation function facilitates higher learning rates, mitigating divergence risks during training.
  • Experimental results demonstrate improved F-scores compared to traditional deep learning methods.

Conclusions:

  • Recurrent Neural Network (RNN) provides a robust framework for hyper-spectral image classification by treating pixels as sequential data.
  • The proposed DL-RNN model offers enhanced performance and accuracy in hyper-spectral image analysis.
  • This research marks a significant advancement in applying sequential data analysis to hyper-spectral imaging.