Abstract
Simultaneously detection and evaluation of small regions of interest from aerial images is successfully achieved by conditional generative adversarial nets (cGAN). As novelty, the paper proposes a cheap and accurate method based on a cGAN structure, containing two generators, and graphics processing units (GPU) to segment small flooded areas and respectively, roads from images taken by unmanned aerial vehicles. In the learning phase the weights for the discriminator and the two generators are established by a back propagation method. The real mask is created by using information of the color components R, G, B, H and a voting scheme in a supervised process. A set of 40 images were used for the learning phase and another set of 100 images were used for method validation. The method presents the advantages of accuracy and time processing (especially in the operational phase).
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References
Yulianto, F., Sofan, P., Zubaidah, A., Sukowati, K.A.D., Pasaribu, J.M., Khomarudin, M.R.: Detecting areas affected by flood using multi-temporal ALOS PALSAR remotely sensed data in Karawang, West Java. Indonesia. Nat. Hazards 77, 959–985 (2015)
Pentari, A., Moirogiorgou, K., Livanos, G., Iliopoulou, D., Zervakis, M.: Feature analysis on river flow video data for floating tracers detection. In: Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), 14–17 Octover, Santorini Island, Greece, pp. 287–292 (2014)
Lee, J.N., Kwak, K.C.: A trends analysis of image processing in unmanned aerial vehicle. Int. J. Comput. Inf. Sci. Eng. 8, 261–264 (2014)
Popescu, D., Ichim, L., Stoican, F.: Unmanned aerial vehicle systems for remote estimation of flooded areas based on complex image processing. Sensors 17(3), 446 (2017)
Popescu, D., Ichim, L.: Aerial image segmentation by use of textural features. In: 20th International Conference on System Theory, Control and Computing (ICSTCC), 13–15 October, Sinaia, Romania, pp. 721–726 (2016)
Strigl, D., Kofler, K., Podlipnig, S.: Performance and scalability of GPU-based convolutional neural networks. In: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 17–19 February, Pisa, Italy, pp. 1–8 (2010)
Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv:1406.2661v1 (2014)
Isola, P., Zhu, J.-Y., Zhou, T., Efros A.A.: Image-to-image translation with Conditional Adversarial Networks. arXiv:1611.07004v1 (2016)
Mirza, M., Osindero, S.: Conditional Generative Adversarial nets. arXiv:1411.1784v1 (2014)
Kataoka, Y., Matsubara, T., Uehara, K.: Image generation using Generative Adversarial Networks and attention mechanism. In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 26–29 June, Okayama, Japan (2016)
Kohl, S., et al.: Adversarial Networks for the detection of aggressive prostate cancer. arXiv:1702.08014v1 (2017)
Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I.: Generative Adversarial Networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging PP(99) (2017)
Tachibana, R., Matsubara,T., Uehara, K.: Semi-supervised learning using adversarial networks. In: IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 26–29 June, Okayama, Japan (2016)
Yazdani, M.: RemixNet: generative adversarial networks for mixing multiple inputs. In: Semantic Computing (ICSC), 30 January–1 February (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference for Learning Representations, San Diego, pp. 1–15 (2015)
Sumalan, A.L., Popescu, D., Ichim, L.: Flood evaluation in critical areas by UAV. In: 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 30 June–2 July, pp. 1–6 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: MIC-CAI, pp. 234–241 (2015)
Torch | Scientific computing for LuaJIT. http://torch.ch/
Documentation - Our Documentation. https://www.python.org/doc/
NumPy — NumPy. http://www.numpy.org/
Acknowledgements
The work has been funded by project CAMIA, GEX 25/2017 (UPB), MAARS 185/2017 (ROSA) and MUWI 1224/2018 (NETIO).
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Popescu, D., Ichim, L., Docea, A. (2018). Complex Conditional Generative Adversarial Nets for Multiple Objectives Detection in Aerial Images. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_59
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