Title | ||
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Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2018 | 10.3390/s18124391 | SENSORS |
Keywords | Field | DocType |
hyperspectral imaging,waxy maize,variety classification,t-SNE,procrustes analysis | Pattern recognition,Image based,Procrustes analysis,Electronic engineering,Hyperspectral imaging,Artificial intelligence,Engineering | Journal |
Volume | Issue | ISSN |
18 | 12.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Aimin Miao | 1 | 0 | 1.35 |
Jiajun Zhuang | 2 | 0 | 1.01 |
Yu Tang | 3 | 44 | 9.61 |
Yong He | 4 | 12 | 4.03 |
Xuan Chu | 5 | 0 | 0.68 |
Shaoming Luo | 6 | 0 | 1.69 |