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

Nikolas Hesse; Christoph Bodensteiner; Michael Arens

doi:10.1117/12.2067179

13 October 2014 Performance evaluation of image-based location recognition approaches based on large-scale UAV imagery

Nikolas Hesse, Christoph Bodensteiner, Michael Arens

Proceedings Volume 9250, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II; 92500N (2014) https://doi.org/10.1117/12.2067179
Event: SPIE Security + Defence, 2014, Amsterdam, Netherlands

Abstract

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.

Citation Download Citation

Nikolas Hesse, Christoph Bodensteiner, and Michael Arens "Performance evaluation of image-based location recognition approaches based on large-scale UAV imagery", Proc. SPIE 9250, Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II, 92500N (13 October 2014); https://doi.org/10.1117/12.2067179

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KEYWORDS

Databases

Visualization

Infrared imaging

Unmanned aerial vehicles

Sensors

RGB color model

Infrared radiation

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