Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, Multi-Head Self- Attention, Convolu...

Celý popis

Podrobná bibliografie

Vydáno v:	2024 International Conference on Data Science and Network Security (ICDSNS) s. 01 - 04
Hlavní autoři:	Naga Saranya, N., Vivekanandhan, V., Al-Farouni, Mohammed, Shreyas, A V, Ranjith kumar, Gotte
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 26.07.2024
Témata:	Accuracy Convolutional neural networks Crops Degradation Error analysis Feature extraction Filters high-resolution hyperspectral images Hyperspectral imaging multi-head self-attention Noise residual learning residual network Residual neural networks
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!