Titre :	Classification of Remote Sensing Images using Transfer Learning and CNN Model with Attention
Type de document :	texte imprimé
Auteurs :	Yousra Halfaya, Auteur ; Wafa Lamamra, Auteur ; Abdelouaheb Moussaoui, Directeur de thèse
Année de publication :	2022
Importance :	1 vol (98 f .)
Langues :	Français (fre)
Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Remote sensing Scene classification
Index. décimale :	004 Informatique
Résumé :	Classification of high-resolution remote sensing image is one of the advanced technologies in our century. However, scientist’s efforts were made to progress various forms of scene classification methods. In this thesis we will focus to identify Aerial image scene classification and fire forest image classification based on Deep Learning approaches. In addition, we will offer at the beginning a comprehensive introduction about of the recent state-of-the-art models. Then we will be proposing two datasets. Firstly, a wide-scale dataset, called “UCMerced” dataset which is available in the public for remote sensing image scene classification. This dataset includes 2,100 images representing 21 scene classes, with each class contains 100 images. Secondly we created a single integrated forest fire dataset containing 2955 images, with 1695 forest fire images and 1260 non-fire image . Based on this dataset, we propose four models based on Transfert Learning models, a ConvNet-based model, a ConvNet-based model with attention mechanism, and Transformers based model. Finally, The performance of the considered models is evaluated by computing the common performance measures.The result of the experimental evaluation show that our proposed models improve scene classification on both datasets.
Côte titre :	MAI/0586
En ligne :	https://drive.google.com/file/d/1CqlypwIur_Ypctq4H6IJob-N1Sq1ll_y/view?usp=share [...]
Format de la ressource électronique :	pdf

Classification of Remote Sensing Images using Transfer Learning and CNN Model with Attention [texte imprimé] / Yousra Halfaya, Auteur ; Wafa Lamamra, Auteur ; Abdelouaheb Moussaoui, Directeur de thèse . - 2022 . - 1 vol (98 f .).
Langues : Français (fre)

Catégories :	Thèses & Mémoires:Informatique
Mots-clés :	Remote sensing Scene classification
Index. décimale :	004 Informatique
Résumé :	Classification of high-resolution remote sensing image is one of the advanced technologies in our century. However, scientist’s efforts were made to progress various forms of scene classification methods. In this thesis we will focus to identify Aerial image scene classification and fire forest image classification based on Deep Learning approaches. In addition, we will offer at the beginning a comprehensive introduction about of the recent state-of-the-art models. Then we will be proposing two datasets. Firstly, a wide-scale dataset, called “UCMerced” dataset which is available in the public for remote sensing image scene classification. This dataset includes 2,100 images representing 21 scene classes, with each class contains 100 images. Secondly we created a single integrated forest fire dataset containing 2955 images, with 1695 forest fire images and 1260 non-fire image . Based on this dataset, we propose four models based on Transfert Learning models, a ConvNet-based model, a ConvNet-based model with attention mechanism, and Transformers based model. Finally, The performance of the considered models is evaluated by computing the common performance measures.The result of the experimental evaluation show that our proposed models improve scene classification on both datasets.
Côte titre :	MAI/0586
En ligne :	https://drive.google.com/file/d/1CqlypwIur_Ypctq4H6IJob-N1Sq1ll_y/view?usp=share [...]
Format de la ressource électronique :	pdf