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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Bibliographic Details

Published in:	2024 International Conference on Data Science and Network Security (ICDSNS) pp. 01 - 04
Main Authors:	Naga Saranya, N., Vivekanandhan, V., Al-Farouni, Mohammed, Shreyas, A V, Ranjith kumar, Gotte
Format:	Conference Proceeding
Language:	English
Published:	IEEE 26.07.2024
Subjects:	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 Access:	Get full text
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