Abstract
Accelerated by the proliferation of small, affordable, and lightweight electronically scanning radar systems as well as advances in Unmanned Aircraft System (UAS) technology, Geo-Registered Radar Returns data are becoming an incredible source for geolocalization in GPS-denied UAS navigation. Most existing approaches match aerial images to pre-stored Digital Elevation Models (DEMs) through 3D terrain reconstruction or GPU-based terrain rendering techniques. However, these reconstruction or rendering processes are themselves error-prone and time-consuming, which further decrease UAS navigation accuracy. In this work, we propose a novel geolocalization approach by directly matching aerial images to DEMs. Inspired by success of deep learning in face recognition/verification, we develop a triplet-ranking network to embed aerial images and DEMs into the same low-dimensional feature space, where matching Aerial-DEM are near one another and mismatched Aerial-DEM are far apart. To create large-scale training dataset, we design an efficient terrain generation approach using per-pixel displacement mapping technique. This approach augments aerial datasets by simulating visual appearances of terrain under different lighting conditions. Experiments are conducted to show the effectiveness of our deep network in finding matches between aerial images and DEMs.
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27 February 2020
The articles listed below were published in Issue January 2020, Issue 1, instead of Issue February 2020, Issues 1–2.
References
Rodriguez, J.J., Aggarwal, J.K.: Matching aerial images to 3D terrain maps. IEEE Trans. Pattern Anal. Mach. Intell. 12(12), 1138–1149 (1990)
Wang, T., Celik, K., Somani, A.K.: Characterization of mountain drainage patterns for GPS-denied UAS navigation augmentation. Comput. Vis. Appl. 27, 87–101 (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: A unified embedding for face recognition and clustering. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2015)
Wang, T., Sun, C., Lao, G.: Aerial-DEM geolocalization for GPS-denied UAS navigation. In: Proceedings of International Conference on Intelligent Control and Information Processing (2018)
Horn, B.K.P., Bachman, B.L.: Using synthetic images to register real images with surface model. Graphics Image Process. 21, 914–924 (1978)
Chen, D.M., Baatz, G., Koser, K., Tasi, S.S., Vedantham, R., et al.: City-scale landmark identification on mobile devices. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (2011)
Li, Y., Snavely, N., Huttenlocher, D.P.: Location recognition using prioritized feature matching. In: Proceedings of European Conference on Computer Vision (2010)
Zamir, A.R., Shah, M.: Accurate image localization based on google maps street view. In: Proceedings of European Conference on Computer Vision (2010)
Bansal, M., Daniilidis, K.: Ultra-wide baseline facade matching for geo-localization. In: Proceedings of European Conference on Computer Vision (2012)
Shan, Q., Wu, C., Curless, B.: Accurate geo-referenced by ground-to-aerial image matching. In: Proceedings of International Conference on 3D Vision (2014)
Lin, T.Y., Cui, Y., Belongi, S., Hays, J.: Learning deep representations fro ground-to-aerial localization. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2015)
Costea, D., Leordeanu, M.: Aerial image geolocalization from recognition and matching of roads and intersections, arXiv preprint arXiv:1605.08323 (2016)
Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J.: Learning fine-grained image similarity with deep ranking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2014)
Yu, Q., Liu, F., Song, Y., et al.: Sketch me that shoe. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.: Image classification with deep convolutional neural networks. In: Proceedings of Neural Information Systems (2012)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. In: Proceedings of International Conference on Learning Representations (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2016)
Szegedy, C., Ioffer, S., Vanhoucke, V.: Inception-v4, Inception-ResNet and the impact of residual connections on learning, arXiv preprint arXiv:1602.07261 (2016)
Ritter, M.E.: An Introduction to Physical Geography and the Environment. Prentice Hall, Upper Saddle River (2006)
Farr, T.G., Rosen, P.A., Caro, E., et al.: The shuttle radar topography mission. Rev. Geophys. 45(2), 1–33 (2007)
Szirmay-Kalos, L., Umenhoffer, T.: Displacement mapping on the GPU-state of the art. Comput. Graphics Forum 27(6), 1567–1592 (2006)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–1120 (2004)
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (2004)
Koch, G., Zemel, R., Salakutdinov, R.: Siamese neural networks for one-shot image recognition. In: Proceedings of ICML Deep Learning Workshop (2017)
Acknowledgements
The research of the first author is funded by National Science Funding of China under Grant 61803084. The research of the second author is funded by Phillip and Viginia Sproul Endowment at Iowa State University.
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Wang, T., Somani, A.K. Aerial-DEM geolocalization for GPS-denied UAS navigation. Machine Vision and Applications 31, 3 (2020). https://doi.org/10.1007/s00138-019-01052-6
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DOI: https://doi.org/10.1007/s00138-019-01052-6