| Autor: |
Fursov, Vladimir Alekseyevich, Bibikov, Sergey Alekseyevich, Zherdev, Denis Alekseevich, Kazanskiy, Nikolay Lvovich |
| Zdroj: |
International Journal of Engineering Systems Modelling and Simulation; 2020, Vol. 11 Issue: 4 p186-193, 8p |
| Abstrakt: |
In the study, a classification algorithm of plant crops in hyperspectral images is analysed. The algorithm uses the conjugation index with a subspace formed by samples of a given class. The purpose of the work is to show that this algorithm, with the data pre-processing (weighting of the feature vectors components and forming of the subclasses), provides a higher classification quality compared to the most popular reference vector method (SVM). The experiments were conducted with the implementation of the SVM method. The Indian Pines test of close types of vegetation, including 16 marked classes of plant crops, was used in the recognition experiments. The test was rather complicated, as class samples are highly correlated. The results show the possibility of a reliable recognition of plant crops. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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