A novel framework combining band selection algorithm and improved 3D prototypical network for tree species classification using airborne hyperspectral images

•A novel framework for obtaining optimal classification results using airborne HSI.•The FAST 3D-CNN P-Net method can improve the feature extraction process.•The CNN-based band selection algorithm performs better in dimensionality reduction.•The proposed framework exhibits optimal performance on seve...

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Vydané v:	Computers and electronics in agriculture Ročník 219; s. 108813
Hlavní autori:	Wu, Jing, Chen, Long, Wang, Jiaqi, Li, Yunfan, Chen, Erxue, Zhang, Xiaoli
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.04.2024
Predmet:	agriculture Airborne hyperspectral images algorithms Band selection Convolutional neural network data collection electronics forest trees forests inventories Multiple tree species classification Prototypical network Prototypical network Band selection Multiple tree species classification Convolutional neural network Airborne hyperspectral images
ISSN:	0168-1699, 1872-7107
On-line prístup:	Získať plný text
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