A Novel Network-Level Fusion Architecture of Proposed Self-Attention and Vision Transformer Models for Land Use and Land Cover Classification From Remote Sensing Images

A Novel Network-Level Fusion Architecture of Proposed Self-Attention and Vision Transformer Models for Land Use and Land Cover Classification From Remote Sensing Images

Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of feature learning in remote sensing for land use and cover classification, as demonstrated by recent deep learning techniques driven by vast amounts of data. In this work, we proposed a new network-level fusion d...

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Bibliographic Details
Main Authors: Saddaf Rubab, Muhammad Attique Khan, Ameer Hamza, Hussain Mobarak Albarakati, Oumaima Saidani, Amal Alshardan, Areej Alasiry, Mehrez Marzougui, Yunyoung Nam
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Augmentation
explainable AI
fusion
land cover
remote sensing (RS)
self-attention
Online Access:https://ieeexplore.ieee.org/document/10595420/

Internet

https://ieeexplore.ieee.org/document/10595420/

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