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...

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Veröffentlicht in:	2024 International Conference on Data Science and Network Security (ICDSNS) S. 01 - 04
Hauptverfasser:	Naga Saranya, N., Vivekanandhan, V., Al-Farouni, Mohammed, Shreyas, A V, Ranjith kumar, Gotte
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 26.07.2024
Schlagworte:	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
Online-Zugang:	Volltext
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