Cargando…
Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis
Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308723/ https://www.ncbi.nlm.nih.gov/pubmed/30545028 http://dx.doi.org/10.3390/s18124391 |
_version_ | 1783383255934828544 |
---|---|
author | Miao, Aimin Zhuang, Jiajun Tang, Yu He, Yong Chu, Xuan Luo, Shaoming |
author_facet | Miao, Aimin Zhuang, Jiajun Tang, Yu He, Yong Chu, Xuan Luo, Shaoming |
author_sort | Miao, Aimin |
collection | PubMed |
description | Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis. |
format | Online Article Text |
id | pubmed-6308723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63087232019-01-04 Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis Miao, Aimin Zhuang, Jiajun Tang, Yu He, Yong Chu, Xuan Luo, Shaoming Sensors (Basel) Article Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis. MDPI 2018-12-11 /pmc/articles/PMC6308723/ /pubmed/30545028 http://dx.doi.org/10.3390/s18124391 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Miao, Aimin Zhuang, Jiajun Tang, Yu He, Yong Chu, Xuan Luo, Shaoming Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title | Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title_full | Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title_fullStr | Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title_full_unstemmed | Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title_short | Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis |
title_sort | hyperspectral image-based variety classification of waxy maize seeds by the t-sne model and procrustes analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308723/ https://www.ncbi.nlm.nih.gov/pubmed/30545028 http://dx.doi.org/10.3390/s18124391 |
work_keys_str_mv | AT miaoaimin hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis AT zhuangjiajun hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis AT tangyu hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis AT heyong hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis AT chuxuan hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis AT luoshaoming hyperspectralimagebasedvarietyclassificationofwaxymaizeseedsbythetsnemodelandprocrustesanalysis |