Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Modèle de classification optimal et entièrement connecté basé sur les réseaux de neurones profonds pour des images hyperspectrales prises par des drones

Manar Ahmed Hamzaa Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaCorrespondence[email protected]

Jaber S. Alzahranib Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Makkah, Saudi Arabia

Amal Al-Rasheedc Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, 11671, Saudi Arabia

Reem Alshahranid Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif, 21944, Saudi Arabia

Mohammad Alamgeere Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha, Saudi Arabia

Abdelwahed Motwakela Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Ishfaq Yaseena Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Mohamed I. Eldesoukif Department of Information System, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

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Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Modèle de classification optimal et entièrement connecté basé sur les réseaux de neurones profonds pour des images hyperspectrales prises par des drones

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Optimal and Fully Connected Deep Neural Networks Based Classification Model for Unmanned Aerial Vehicle Using Hyperspectral Remote Sensing Images

Modèle de classification optimal et entièrement connecté basé sur les réseaux de neurones profonds pour des images hyperspectrales prises par des drones

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