Performance evaluation of image-based location recognition approaches based on large-scale UAV imagery | Zendy

Nikolas Hesse | Zendy; Christoph Bodensteiner | Zendy; Michael Arens | Zendy

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Performance evaluation of image-based location recognition approaches based on large-scale UAV imagery

Author(s) -

Nikolas Hesse,

Christoph Bodensteiner,

Michael Arens

Publication year - 2014

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2067179

Subject(s) - computer science , artificial intelligence , computer vision , scale invariant feature transform , cognitive neuroscience of visual object recognition , scale (ratio) , affine transformation , hessian matrix , visualization , pattern recognition (psychology) , object (grammar) , image (mathematics) , mathematics , pure mathematics , physics , quantum mechanics

Recognizing the location where an image was taken, solely based on visual content, is an important problem in computer vision, robotics and remote sensing. This paper evaluates the performance of standard approaches for location recognition when applied to large-scale aerial imagery in both electro-optical (EO) and infrared (IR) domains. We present guidelines towards optimizing the performance and explore how well a standard location recognition system is suited to handle IR data. We show on three datasets that the performance of the system strongly increases if SIFT descriptors computed on Hessian-Affine regions are used instead of SURF features. Applications are widespread and include vision-based navigation, precise object geo-referencing or mapping

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