Geo-localization using Volumetric Representations of Overhead Imagery
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This paper addresses the problem of determining the location of a ground level image by using geo-referenced overhead imagery. The input query image is assumed to be given with no meta-data and the content of the image is to be matched to a priori constructed reference representations. The semantic breakdown of the content of the query image is provided through manual labeling; however, all processing involving the reference imagery and matching are fully automated. In this paper, a volumetric representation is proposed to fuse different modalities of overhead imagery and construct a 3D reference world. Attributes of this reference world such as orientation of the world surfaces, types of land cover, depth order of fronto-parallel surfaces are indexed and matched to the attributes of the surfaces manually marked on the query image. An exhaustive but highly parallelizable matching scheme is proposed and the performance is evaluated on a set of query images located in a coastal region in Eastern United States. The performance is compared to a baseline region reduction algorithm and to a landmark existence matcher that uses a 2D representation of the reference world. The proposed 3D geo-localization framework performs better than the 2D approach for 75 % of the query images.
KeywordsImage search Geo-localization 3D Modeling Visibility index LIDAR OpenStreetMap
Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL), Contract FA8650-12-C-7211. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. Government.
- Altwaijry, H., Moghimi, M., & Belongie, S. (2014). Recognizing locations with Google glass: A case study. In Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV).Google Scholar
- Ardeshir, S., Zamir, A. R., Torroella, A., & Shah, M. (2014). GIS-assisted object detection and geospatial localization. In Proceedings of European Conference on Computer Vision (ECCV). Google Scholar
- Aubry, M., Russell, B., & Sivic, J. (2014). Painting-to-3D model alignment via discriminative visual elements. ACM Transactions on Graphics (TOG), 33(2), 14.Google Scholar
- Baatz, G., Saurer, O., Koser, K., & Pollefeys, M. (2012). Large scale visual geo-localization of images in mountainous terrain. In Proceedings of European Conference on Computer Vision (ECCV).Google Scholar
- Bansal, M., & Daniilidis, K. (2014). Geometric urban geo-localization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Bansal, M., Daniilidis, K., & Sawhney, H. (2012). Ultra-wide baseline facade matching for geo-localization. In 1st International Workshop on Visual Analysis and Geo-localization of Large-Scale Imagery, ECCV.Google Scholar
- Calakli, F., Ulusoy, A. O., Restrepo, M. I., & Taubin, G. (2012). High resolution surface reconstruction from multi-view aerial imagery. In Proceedings of 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT).Google Scholar
- Crispell, D., Mundy, J., & Taubin, G. (2011). A variable-resolution probabilistic three dimensional model for change detection. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 489–5000.Google Scholar
- Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., et al. (2011). Completion of the national land cover database for the conterminous United States. PE&RS, 77(9), 858–864.Google Scholar
- Hays, J., & Efros, A. A. (2008). im2gps: Estimating geographic information from a single image. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Irschara, A., Zach, C., Frahm, J., & Bischof, H. (2009). From structure-from-motion point clouds to fast location recognition. In Proceedings of Computer Vision and Pattern Recognition (CVPR). Google Scholar
- Kluckner, S., Mauthner, T., Roth, P., & Bischof, H. (2009). Semantic classification in aerial imagery by integrating appearance and height information. In Proceedings of Asian Conference on Computer Vision (ACCV).Google Scholar
- Lee, S., & Nevatia, R. (2011). Robust camera calibration tool for video surveillance camera in urban environment. In Proceedings of Camera Networks Workshop (CVPR).Google Scholar
- Li, A., Morariu, V. I., & Davis, L. S. (2014). Planar structure matching under projective uncertainty for geolocation. In Proceedings of European Conference on Computer Vision (ECCV).Google Scholar
- Li, Y., Snavely, N., & Huttenlocher, D. (2010). Location recognition using prioritized feature matching. In Proceedings of European Conference on Computer Vision (ECCV).Google Scholar
- Lin, T., Belongie, S., & Hays, J. (2013). Cross-view image geolocalization. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Matei, B., Valk, N. V., Zhu, Z., & Cheng, H. (2013). Image to LIDAR matching for geotagging in urban environments. In Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV).Google Scholar
- Miller, A., Jain, V., & Mundy, J. L. (2011). Real-time rendering and dynamic updating of 3D volumetric data. In Proceedings of the Fourth Workshop on General Purpose Processing on Graphics Processing Units (GPUs).Google Scholar
- NAIP. (2009). Retrieved from http://www.mngeo.state.mn.us/chouse/metadata/naip09.html.
- NOAA. (n.d.). Retrieved from http://www.csc.noaa.gov/digitalcoast/data/coastallidar.
- OSM Map Features. (n.d.). Retrieved from http://wiki.openstreetmap.org/wiki/Map_Features.
- Ozcanli, O. C., Dong, Y., Mundy, J., Webb, H., Hammoud, R., & Victor, T. (2014). Automatic geo-location correction of satellite imagery. In Proceedings of Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Park, M., Chen, Y., & Shafique, K. (2013). Tag configuration Matcher for geo-tagging. In Proceedings of SIGSPATIAL/GIS, pp. 374–377.Google Scholar
- Pollard, T., & Mundy, J. L. (2007). Change detection in a 3D world. In Proceedings of Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Pollard, T., Eden, I., Cooper, D. B., & Mundy, J. L. (2009). A volumetric approach to change detection in satellite images. American Society for Photogrammetry and Remote Sensing.Google Scholar
- Ramalingam, S., Bouaziz, S., Sturm, P., & Brand, M. (2009). Geolocalization using skylines from omni-images. In Proceedings of International Conferences on Computer Vision (ICCV) Workshops.Google Scholar
- Restrepo, M. I., Mayer, B. A., Ulusoy, A. O., & Mundy, J. L. (2012). Characterization of 3D volumetric probabilistic scenes for object recognition. IEEE Journal of Selected Topics in Signal Processing, 6, 522–537.Google Scholar
- Schindler, G., Brown, M., & Szeliski, R. (2007). City-scale location recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Sibbing, D., Sattler, T., Leibe, B., & Kobbelt, L. (2013). SIFT-Realistic Rendering. In Proceeding of 3D Vision (3DV).Google Scholar
- Sobel, E., Vinciguerra, L., Rinehart, M., & Dankert, J. (2012). URGENT phase II final report. BAE Systems, Sponsored by DARPA IPTO.Google Scholar
- Tzeng, E., Zhai, A., Clements, M., Townshend, R., & Zakhor, A. (2013). User-driven geolocation of untagged desert imagery using digital elevation models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, (pp. 237–244).Google Scholar
- Ulusoy, A. O., Biris, O., & Mundy, J. L. (2013). Dynamic probabilistic volumetric models. In Proceedings of International Conference on Computer Vision (ICCV).Google Scholar