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A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification
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 hy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279854/ https://www.ncbi.nlm.nih.gov/pubmed/34306058 http://dx.doi.org/10.1155/2021/9923491 |
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author | Ramirez Rochac, Juan F. Zhang, Nian Thompson, Lara A. Deksissa, Tolessa |
author_facet | Ramirez Rochac, Juan F. Zhang, Nian Thompson, Lara A. Deksissa, Tolessa |
author_sort | Ramirez Rochac, Juan F. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8279854 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-82798542021-07-22 A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification Ramirez Rochac, Juan F. Zhang, Nian Thompson, Lara A. Deksissa, Tolessa Comput Intell Neurosci Research Article 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. Hindawi 2021-07-06 /pmc/articles/PMC8279854/ /pubmed/34306058 http://dx.doi.org/10.1155/2021/9923491 Text en Copyright © 2021 Juan F. Ramirez Rochac et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ramirez Rochac, Juan F. Zhang, Nian Thompson, Lara A. Deksissa, Tolessa A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title | A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title_full | A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title_fullStr | A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title_full_unstemmed | A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title_short | A Robust Context-Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification |
title_sort | robust context-based deep learning approach for highly imbalanced hyperspectral classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8279854/ https://www.ncbi.nlm.nih.gov/pubmed/34306058 http://dx.doi.org/10.1155/2021/9923491 |
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