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Article

UAV Image Multi-Labeling with Data-Efficient Transformers

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Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Applied Computer Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
*
Author to whom correspondence should be addressed.
Academic Editor: Rubén Usamentiaga
Appl. Sci. 2021, 11(9), 3974; https://doi.org/10.3390/app11093974
Received: 27 March 2021 / Revised: 24 April 2021 / Accepted: 25 April 2021 / Published: 27 April 2021
(This article belongs to the Section Computing and Artificial Intelligence)
In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model. View Full-Text
Keywords: multi-label image classification; unmanned aerial vehicles (UAV); vision transformers; data augmentation multi-label image classification; unmanned aerial vehicles (UAV); vision transformers; data augmentation
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MDPI and ACS Style

Bashmal, L.; Bazi, Y.; Al Rahhal, M.M.; Alhichri, H.; Al Ajlan, N. UAV Image Multi-Labeling with Data-Efficient Transformers. Appl. Sci. 2021, 11, 3974. https://doi.org/10.3390/app11093974

AMA Style

Bashmal L, Bazi Y, Al Rahhal MM, Alhichri H, Al Ajlan N. UAV Image Multi-Labeling with Data-Efficient Transformers. Applied Sciences. 2021; 11(9):3974. https://doi.org/10.3390/app11093974

Chicago/Turabian Style

Bashmal, Laila, Yakoub Bazi, Mohamad M. Al Rahhal, Haikel Alhichri, and Naif Al Ajlan. 2021. "UAV Image Multi-Labeling with Data-Efficient Transformers" Applied Sciences 11, no. 9: 3974. https://doi.org/10.3390/app11093974

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