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Open AccessArticle

Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization

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Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Information Science Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
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Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 351; https://doi.org/10.3390/rs10020351
Received: 17 January 2018 / Revised: 13 February 2018 / Accepted: 22 February 2018 / Published: 24 February 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
In this paper, we present a new algorithm for cross-domain classification in aerial vehicle images based on generative adversarial networks (GANs). The proposed method, called Siamese-GAN, learns invariant feature representations for both labeled and unlabeled images coming from two different domains. To this end, we train in an adversarial manner a Siamese encoder–decoder architecture coupled with a discriminator network. The encoder–decoder network has the task of matching the distributions of both domains in a shared space regularized by the reconstruction ability, while the discriminator seeks to distinguish between them. After this phase, we feed the resulting encoded labeled and unlabeled features to another network composed of two fully-connected layers for training and classification, respectively. Experiments on several cross-domain datasets composed of extremely high resolution (EHR) images acquired by manned/unmanned aerial vehicles (MAV/UAV) over the cities of Vaihingen, Toronto, Potsdam, and Trento are reported and discussed. View Full-Text
Keywords: manned/unmanned aerial vehicles (MAV/UAV); extremely high resolution (EHR) images; distribution mismatch; generative adversarial networks (GANs); Siamese encoder–decoder manned/unmanned aerial vehicles (MAV/UAV); extremely high resolution (EHR) images; distribution mismatch; generative adversarial networks (GANs); Siamese encoder–decoder
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MDPI and ACS Style

Bashmal, L.; Bazi, Y.; AlHichri, H.; AlRahhal, M.M.; Ammour, N.; Alajlan, N. Siamese-GAN: Learning Invariant Representations for Aerial Vehicle Image Categorization. Remote Sens. 2018, 10, 351.

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