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pro vyhledávání: '"Ethan Worch"'
Publikováno v:
Journal of Imaging, Vol 8, Iss 5, p 126 (2022)
In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statist
Externí odkaz:
https://doaj.org/article/7377017c50544e4c89c664391287dd4f
Publikováno v:
IGARSS
Deep learning-based approaches for hyperspectral image (HSI) feature extraction and classification have gained popularity in recent years. Effective extraction of spectral and spatial information is desired for classifying HSI using a convolutional n
Publikováno v:
IGARSS
Metaheuristic optimization algorithms have been popular band selection methods in hyperspectral imaging (HSI) over the years due to their ability to find good solutions in reasonable time frames. Nature-inspired metaheuristics imitate processes in na
Publikováno v:
Journal of Imaging; May2022, Vol. 8 Issue 5, p126-126, 21p