New framework for hyperspectral band selection using modified wind-driven optimization algorithm

The presence of irrelevant and highly correlated spectral bands significantly reduces the classification accuracy of the hyperspectral images. Therefore, the selection of suitable bands from the set of available spectral bands plays a crucial role in improving the classification accuracy. In this pa...

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Vydané v:	International journal of remote sensing Ročník 40; číslo 20; s. 7852 - 7873
Hlavní autori:	Sawant, Shrutika S., Manoharan, Prabukumar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Taylor & Francis 18.10.2019 Taylor & Francis Ltd
Predmet:	Accuracy Aesculus air Algorithms Band spectra Classification Colleges & universities Convergence data collection Datasets Deep learning Exploitation Exploration Feature extraction Global optimization Heuristic methods Hyperspectral imaging Image classification Machine learning Performance enhancement Spectral bands system optimization Wind
ISSN:	0143-1161, 1366-5901, 1366-5901
On-line prístup:	Získať plný text
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