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Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis.
In: Sensors (14248220), Jg. 18 (2018-12-01), Heft 12, S. 4391-4391
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
Titel: |
Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis.
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Autor/in / Beteiligte Person: | Miao, Aimin ; Zhuang, Jiajun ; Tang, Yu ; He, Yong ; Chu, Xuan ; Luo, Shaoming |
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Zeitschrift: | Sensors (14248220), Jg. 18 (2018-12-01), Heft 12, S. 4391-4391 |
Veröffentlichung: | 2018 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s18124391 |
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