Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding Using Aerial Remote Sensing Data

Design of Super Resolution and Fuzzy Deep Learning Architecture for the Classification of Land Cover and Landsliding Using Aerial Remote Sensing Data

The diversity, noise, interimage interference, image distortion, and increase in the number of classes in aerial remotely sensed dataset cause exertion in the classification. The efficacy and stability of convolutional neural networks increase in image classification with the specified use of featur...

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Bibliographic Details
Main Authors: Junaid Ali Khan, Muhammad Attique Khan, Mohammed Al-Khalidi, Dina Abdulaziz AlHammadi, Areej Alasiry, Mehrez Marzougui, Yudong Zhang, Faheem Khan
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Aerial remote sensing (RS)
chaotic particle swarm optimization (C-PSO)
fuzzy-CNN deep learning (DL)
Monte Carlo simulations
statistical analysis and model stability
Online Access:https://ieeexplore.ieee.org/document/10741345/
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