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Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing
This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal c...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935556/ https://www.ncbi.nlm.nih.gov/pubmed/33679841 http://dx.doi.org/10.3389/fpls.2021.627865 |
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author | Li, Yu-hang Tan, Xin Zhang, Wei Jiao, Qing-bin Xu, Yu-xing Li, Hui Zou, Yu-bo Yang, Lin Fang, Yuan-peng |
author_facet | Li, Yu-hang Tan, Xin Zhang, Wei Jiao, Qing-bin Xu, Yu-xing Li, Hui Zou, Yu-bo Yang, Lin Fang, Yuan-peng |
author_sort | Li, Yu-hang |
collection | PubMed |
description | This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%. |
format | Online Article Text |
id | pubmed-7935556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79355562021-03-06 Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing Li, Yu-hang Tan, Xin Zhang, Wei Jiao, Qing-bin Xu, Yu-xing Li, Hui Zou, Yu-bo Yang, Lin Fang, Yuan-peng Front Plant Sci Plant Science This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%. Frontiers Media S.A. 2021-02-18 /pmc/articles/PMC7935556/ /pubmed/33679841 http://dx.doi.org/10.3389/fpls.2021.627865 Text en Copyright © 2021 Li, Tan, Zhang, Jiao, Xu, Li, Zou, Yang and Fang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Li, Yu-hang Tan, Xin Zhang, Wei Jiao, Qing-bin Xu, Yu-xing Li, Hui Zou, Yu-bo Yang, Lin Fang, Yuan-peng Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title | Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title_full | Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title_fullStr | Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title_full_unstemmed | Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title_short | Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing |
title_sort | research and application of several key techniques in hyperspectral image preprocessing |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935556/ https://www.ncbi.nlm.nih.gov/pubmed/33679841 http://dx.doi.org/10.3389/fpls.2021.627865 |
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