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

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

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 domain...

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Bibliographic Details
Main Authors: Laila Bashmal, Yakoub Bazi, Haikel AlHichri, Mohamad M. AlRahhal, Nassim Ammour, Naif Alajlan
Format: Article
Language:English
Published: MDPI AG 2018-02-01
Series:Remote Sensing
Subjects:
manned/unmanned aerial vehicles (MAV/UAV)
extremely high resolution (EHR) images
distribution mismatch
generative adversarial networks (GANs)
Siamese encoder–decoder
Online Access:http://www.mdpi.com/2072-4292/10/2/351

Internet

http://www.mdpi.com/2072-4292/10/2/351

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