참고문헌
- Amhar, F., Jansa, J., and Ries, C. (1998), The generation of true orthophotos using a 3d building model in conjunction with a conventional DTM, International Archives of Photogrammetry and Remote Sensing, Vol. 32, pp. 16-22.
- Arjovsky, M., Chintala, S., and Bottou, L. (2017), Wasserstein generative adversarial networks, International Conference on Machine Learning-2017, 6-11 August, Sydney, Australia, pp. 214-223.
- Bashmal, L., Bazi, Y., AlHichri, H., AlRahhal, M., Ammour, N., and Alajlan, N. (2018), Siamese-GAN: Learning invariant representations for aerial vehicle image categorization, Remote Sensing, Vol. 10, No. 2, pp. 351. https://doi.org/10.3390/rs10020351
- Behnke, S. (2003), Hierarchical Neural Networks for Image Interpretation, Springer-Verlag, Berlin, Germany.
- Cramer, M. (2010), The DGPF test on digital aerial camera evaluation - Overview and test design. Photogrammetrie, Fernerkundung, Geoinformation, Vol. 2, pp. 73-82. https://doi.org/10.1127/1432-8364/2010/0041
- Demir, U. and Unal, G. (2018), Patch-Based image inpainting with generative adversarial networks, arXiv:1803.07422v1.
- Demiray, B., Sit, M., and Demir, I. (2020), D-SRGAN: DEM super-resolution with generative adversarial networks, arXiv:2004.04788v2.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014), Generative adversarial nets, Neural Information Processing Systems, pp. 2672-2680.
- Gui, J., Sun, Z., Wen, Y., Tao, D., and Ye, J. (2020), A review on generative adversarial networks: algorithms, theory, and applications, arXiv:2001.06937v1.
- Habib, A., Kim, E.M., and Kim, C.J. (2007), New methodologies for true orthophoto generation, Photogrammetric Engineering & Remote Sensing, Vol. 73, No. 1, pp. 25-36. https://doi.org/10.14358/PERS.73.1.25
- Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Klambauer, G., and Hochreiter, S. (2017), GANs trained by a two time-scale update rule converge to a Nash equilibrium, arXiv:1706.08500v6.
- Hyung, S., Shin, Y., and Lee, D.C. (2020), Artificial neural network for IR image to true color image conversion, Proceedings of Korean Society of Surveying, Geodesy, Photogrammetry & Cartography-2020, 2-3 July, Seoul, Korea, pp. 212-217. (in Korean with English abstract)
- Isola, P., Zhu, J.Y., Zhou, T., and Efros, A. (2017), Image-toimage translation with conditional adversarial networks, IEEE Conference on Computer Vision and Pattern Recognition-2017, 21-26 July, Honolulu, HI, USA, pp. 1125-1134.
- Kim, E., Choi, H., and Park, J. (2017), Analysis of applicability of orthophoto using 3d mesh on aerial image with large file size, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 35, No. 3, pp. 155-166. https://doi.org/10.7848/ksgpc.2017.35.3.155
- Kim, J. and Um, D. (2015), High quality ortho-image production using the high resolution DMC aerial image, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 33, No. 1, pp. 11-21. https://doi.org/10.7848/ksgpc.2015.33.1.11
- LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., and Jackel, L.D. (1989), Backpropagation applied to handwritten zip code recognition, Neural Computation, Vol. 4, No. 1, pp. 541-551.
- Lee, H. (2008), Producing true orthophoto using multidimensional spatial information, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 26, No. 3, pp. 241-253.
- Li, C. and Wand, M. (2016), Precomputed real-time texture synthesis with markovian generative adversarial networks, European Conference on Computer Vision-2016, 11-14 October, Amsterdam, The Netherlands, pp. 702-716.
- Mirza, M. and Osindero, S. (2014), Conditional generative adversarial nets, arXiv:1411.1784.
- Panagiotou, E., Chochlakis, G., Grammatikopoulos, L., and Charou, E. (2020), Generating elevation surface from a single RGB remotely sensed image using deep learning, Remote Sensing, Vol. 12, No. 12.
- Radford, A., Metz, L., and Chintala, S. (2015), Unsupervised representation learning with deep convolutional generative adversarial networks, arXiv:1511.06434.
- Ronneberger, O., Fischer, P., and Brox, T. (2015), U-Net: Convolutional networks for biomedical image segmentation, Medical Image Computing and Computer-Assisted Intervention, pp. 234-241.
- Rottensteiner, F., Sohn, G., Gerke, M., and Wegner, J. (2013), ISPRS test project on urban classification and 3D building reconstruction, http://www2.isprs.org/tl_files/isprs/wg34/docs/ComplexScenes_revision_v4.pdf (last date accessed: 6 April 2018).
- Sajjadi, M., Scholkopf, B., and Hirsch, M. (2017), Enhancenet: Single image super-resolution through automated texture synthesis, The IEEE International Conference on Computer Vision-2017, 22-29 October, Venice, Italy, pp. 4501-4510.
- Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016), Improved Techniques for Training GANs, arXiv:1606.03498v1.
- Shelhamer, E., Long, J., and Darrell, T. (2017), Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 4, pp. 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
- Simard, P., Steinkraus, D., and Platt, J. (2003), Best practices for convolutional neural networks applied to visual document analysis, International Conference on Document Analysis and Recognition-2003, 3-6 August, Edinburgh, Scotland, UK, pp. 958-963.
- Vaananen, P. (2019), Removing 3D Point Cloud Occlusion Artifacts with Generative Adversarial Networks, PhD thesis, Department of Computer Science, University of Helsinki, Helsinki, Finland, 56p.
- Wang, A., Li, Y., Jiang, K., Zhao, L., and Iwahori, Y. (2019), Lidar data classification algorithm based on generative adversarial network, IEEE International Geoscience and Remote Sensing Symposium, pp. 2487-2490.
- Yarlagadda, S., Guera, D., Bestagini, P., Zhu, F., Tubaro, S., and Delp, E. (2018), Satellite image forgery detection and localization using GAN and one-class classifier, Electronic Imaging, Vol. 2018, No. 7, pp. 214-1-214-9.
- Yoo, E. and Lee D.C. (2016), True orthoimage generation by mutual recovery of occlusion areas, GIScience & Remote Sensing, Vol. 53, No. 2, pp. 227-246. https://doi.org/10.1080/15481603.2015.1128629