A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications.

Ghanbari, Hamid; Mahdianpari, Masoud; Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 et Mohammadimanesh, Fariba (2021). A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14 . pp. 3602-3613. DOI: 10.1109/JSTARS.2021.3065569.

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Résumé

Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This article presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including: 1) general characteristics, such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications, such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, and then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

Type de document:	Article
Mots-clés libres:	convolutional neural network (CNN); deep learning (DL); meta-analysis; remote sensing (RS)
Centre:	Centre Eau Terre Environnement
Date de dépôt:	10 juin 2021 15:23
Dernière modification:	08 févr. 2022 21:54
URI:	https://espace.inrs.ca/id/eprint/11764

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