Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ...
Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-class variability and the different scales and orientations of the objects presen...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2303.11389 https://arxiv.org/abs/2303.11389 |
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ftdatacite:10.48550/arxiv.2303.11389 2024-06-09T07:45:36+00:00 Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... Faria, Fabio A. Buris, Luiz H. Pereira, Luis A. M. Cappabianco, Fábio A. M. 2023 https://dx.doi.org/10.48550/arxiv.2303.11389 https://arxiv.org/abs/2303.11389 unknown arXiv Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 Computer Vision and Pattern Recognition cs.CV Machine Learning cs.LG FOS Computer and information sciences CreativeWork article Preprint Article 2023 ftdatacite https://doi.org/10.48550/arxiv.2303.11389 2024-05-13T10:44:41Z Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-class variability and the different scales and orientations of the objects present in the dataset images. In remote sensing area, the use of CNN architectures as an alternative solution is also a reality for scene classification tasks. Generally, these CNNs are used to perform the traditional image classification task. However, another less used way to classify remote sensing image might be the one that uses deep metric learning (DML) approaches. In this sense, this work proposes to employ six DML approaches for aerial scene classification tasks, analysing their behave with four different pre-trained CNNs as well as combining them through the use of evolutionary computation algorithm (UMDA). In performed experiments, it is possible to observe than DML approaches can achieve the best ... : 9 pages, 4 figures, accepted for presentation at the GECCO2024 ... Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) Umda ENVELOPE(36.410,36.410,62.808,62.808) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
unknown |
topic |
Computer Vision and Pattern Recognition cs.CV Machine Learning cs.LG FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV Machine Learning cs.LG FOS Computer and information sciences Faria, Fabio A. Buris, Luiz H. Pereira, Luis A. M. Cappabianco, Fábio A. M. Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
topic_facet |
Computer Vision and Pattern Recognition cs.CV Machine Learning cs.LG FOS Computer and information sciences |
description |
Aerial scene classification, which aims to semantically label remote sensing images in a set of predefined classes (e.g., agricultural, beach, and harbor), is a very challenging task in remote sensing due to high intra-class variability and the different scales and orientations of the objects present in the dataset images. In remote sensing area, the use of CNN architectures as an alternative solution is also a reality for scene classification tasks. Generally, these CNNs are used to perform the traditional image classification task. However, another less used way to classify remote sensing image might be the one that uses deep metric learning (DML) approaches. In this sense, this work proposes to employ six DML approaches for aerial scene classification tasks, analysing their behave with four different pre-trained CNNs as well as combining them through the use of evolutionary computation algorithm (UMDA). In performed experiments, it is possible to observe than DML approaches can achieve the best ... : 9 pages, 4 figures, accepted for presentation at the GECCO2024 ... |
format |
Article in Journal/Newspaper |
author |
Faria, Fabio A. Buris, Luiz H. Pereira, Luis A. M. Cappabianco, Fábio A. M. |
author_facet |
Faria, Fabio A. Buris, Luiz H. Pereira, Luis A. M. Cappabianco, Fábio A. M. |
author_sort |
Faria, Fabio A. |
title |
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
title_short |
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
title_full |
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
title_fullStr |
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
title_full_unstemmed |
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification ... |
title_sort |
creating ensembles of classifiers through umda for aerial scene classification ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2303.11389 https://arxiv.org/abs/2303.11389 |
long_lat |
ENVELOPE(36.410,36.410,62.808,62.808) |
geographic |
Umda |
geographic_facet |
Umda |
genre |
DML |
genre_facet |
DML |
op_rights |
Creative Commons Attribution Non Commercial No Derivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode cc-by-nc-nd-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2303.11389 |
_version_ |
1801375036790013952 |