A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture

AI-driven precision agriculture applications can benefit from the large data source that remote sensing (RS) provides, as it can gather agricultural monitoring data at various scales throughout the year. Numerous advantages for sustainable agricultural applications, including yield prediction, crop...

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Bibliographic Details
Main Authors: Hussain Mobarak Albarakati, Muhammad Attique Khan, Ameer Hamza, Faheem Khan, Naoufel Kraiem, Leila Jamel, Latifah Almuqren, Roobaea Alroobaea
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Agriculture
data augmentation
deep learning
optimization
remote sensing (RS)
self-attention
Online Access:https://ieeexplore.ieee.org/document/10452788/
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