A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification.

Journal: Computational intelligence and neuroscience
Published Date: Jul 6, 2021

Abstract

Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

Authors

  • Juan F Ramirez Rochac
    Department of Computer Science & Information Technology, University of the District of Columbia, Washington, DC 20008, USA.
  • Nian Zhang
    Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, D. C., SC 20008, USA.
  • Lara A Thompson
    Biomedical Engineering Program, Department of Mechanical Engineering, University of the District of Columbia, Washington, DC 20008, USA.
  • Tolessa Deksissa
    Water Resources Research Institute, University of the District of Columbia, Washington, DC 20008, USA.

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