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Authors: Emna Brahim 1 ; Sonia Bouzidi 1 ; 2 and Walid Barhoumi 1 ; 3

Affiliations: 1 Université de Tunis El Manar, Institut Supérieur d’Informatique d’El Manar, Research Team on Intelligent Systems in Imaging and Artificial Vision (SIIVA), LR16ES06 Laboratoire de Recherche en Informatique, Modélisation et Traitement de l’Information et de la Connaissance (LIMTIC), 2080 Ariana, Tunisia ; 2 Université de Carthage, Institut National des Sciences Appliquées et de Technologie, 1080 Centre Urbain Nord BP Tunis Cedex, Tunisia ; 3 Université de Carthage, Ecole Nationale d’Ingénieurs de Carthage, 45 Rue des Entrepreneurs, 2035 Tunis-Carthage, Tunisia

Keyword(s): Change Detection, CNN, ResNet152, Shearlet Transform.

Abstract: In this paper, we present an effective method to extract the change in two optical remote-sensing images. The proposed method is mainly composed of the following steps. First, the two input Normalized Difference Vegetation Index (NDVI) images are smoothed using the Shearlet transform. Then, we used ResNet152 architecture in order to extract the final change detection image. We validated the performance of the proposed method on three challenging data illustrating the areas of Brazil, Virginia, and California. The experiments performed on 38416 patches showed that the suggested method has outperformed many relevant state-of-theart works with an accuracy of 99.50%.

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Paper citation in several formats:
Brahim, E.; Bouzidi, S. and Barhoumi, W. (2023). ResNet Classifier Using Shearlet-Based Features for Detecting Change in Satellite Images. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 427-434. DOI: 10.5220/0011781200003417

@conference{visapp23,
author={Emna Brahim. and Sonia Bouzidi. and Walid Barhoumi.},
title={ResNet Classifier Using Shearlet-Based Features for Detecting Change in Satellite Images},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={427-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011781200003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - ResNet Classifier Using Shearlet-Based Features for Detecting Change in Satellite Images
SN - 978-989-758-634-7
IS - 2184-4321
AU - Brahim, E.
AU - Bouzidi, S.
AU - Barhoumi, W.
PY - 2023
SP - 427
EP - 434
DO - 10.5220/0011781200003417
PB - SciTePress

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