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

: Hesse, Nikolas; Bodensteiner, Christoph; Arens, Michael

Fulltext urn:nbn:de:0011-n-3106838 (690 KByte PDF)
MD5 Fingerprint: 95e3b06c6acebaa71827db0633d2086a
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Created on: 28.10.2014

Bishop, G. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II : 13.10.2014, Amsterdam
Bellingham, WA: SPIE, 2014 (Proceedings of SPIE 9250)
ISBN: 978-1-62841-313-7
Paper 92500N, 6 pp.
Conference "Electro-Optical Remote Sensing, Photonic Technologies, and Applications" <8, 2014, Amsterdam>
Conference "Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing" <2, 2014, Amsterdam>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
location recognition; image retrieval; remote sensing; UAV imagery

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.