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Erschienen in:
Numerical Algorithms for the Parametric Continuation of Stiff ODEs Deriving from the Modeling of Combustion with Detailed Chemical Mechanisms Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Learning Aerial Image Similarity Using Triplet Networks

verfasst von : Vytautas Valaitis, Virginijus Marcinkevicius, Rokas Jurevicius

Erschienen in: Numerical Computations: Theory and Algorithms

Verlag: Springer International Publishing

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Abstract

Unmanned aerial vehicles (UAV) faces localization challenges in satellite navigation systems denied environments. Images taken from on-board cameras can be used to compare against orthophotographical map to support visual localization algorithms. Image similarity estimation can be achieved calculating various similarity metrics. Pearson correlation was found to be the best choice for evaluating areal images similarity in our experiments. Still is not robust against image displacement caused by aircraft frame movement. We propose a new architecture of triplet neural network to learn image similarity measure. The proposed architecture incorporates VGG16 network base layers. Top layer structure, loss function and performance metrics being suggested by authors. Images were matched to the maps from satellite photo. The matching results from proposed neural network architecture were compared and evaluated against Pearson correlation.

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Vorheriges Kapitel Epidemic Spreading Curing Strategy Over Directed Networks
Nächstes Kapitel In the Quest for Invariant Structures Through Graph Theory, Groups and Mechanics: Methodological Aspects in the History of Applied Mathematics
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Metadaten
Titel
Learning Aerial Image Similarity Using Triplet Networks
verfasst von
Vytautas Valaitis
Virginijus Marcinkevicius
Rokas Jurevicius
Copyright-Jahr
2020
Buch
Numerical Computations: Theory and Algorithms

Print ISBN: 978-3-030-40615-8

Electronic ISBN: 978-3-030-40616-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-40616-5_15