References
- [1] Zhang, C., Pan, X., Li, H., Gardiner, A., Sargent, I., Hare, J., Atkinson, P. M. (2018), A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS Journal of Photogrammetry and Remote Sensing 140, 133–144.
- [2] Pouliot, D., Latifovic, R., Pasher, J., Duffe, J. (2018), Landsat super-resolution enhancement using convolution neural networks and Sentinel-2 for training. Remote Sensing 10(3), art. no 394.
- [3] Shen, Y., Zhou, H., Li, J., Jian, F., Jayas, D. S. (2018), Detection of stored-grain insects using deep learning. Computers and Electronics in Agriculture 145, 319–325.
- [4] Ha, J. G., Moon, H., Kwak, J. T., Hassan, S. I., Dang, M., Lee, O. N., Park, H. Y. (2017), Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Applied Remote Sensing 11(4), art. no 042621.
- [5] Uzal, L. C., Grinblat, G. L., Namías, R., Larese, M. G., Bianchi, J. S., Morandi, E. N., Granitto, P. M. (2018), Seed-per-pod estimation for plant breeding using deep learning. Computers and Electronics in Agriculture 150, 196–204.
- [6] Zhao, T., Wang, Z., Yang, Q., Chen, Y. Q. (2017), Melon yield prediction using small unmanned aerial vehicles. Proceedings of SPIE – The International Society for Optical Engineering 10218, art. no 1021808.
- [7] Li, L., Fan, Y., Huang, X., Tian, L. (2016), Real-time UAV weed scout for selective weed control by adaptive robust control and machine learning algorithm. 2016 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2016.
- [8] Bejiga, M. B., Zeggada, A., Nouffidj, A., Melgani, F. (2017), A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery. Remote Sensing 9(2), art. no 100.
- [9] Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., Zuair, M. (2017), Deep learning approach for car detection in UAV imagery. Remote Sensing 9(4), art. no 312.
- [10] Andrea, C.-C., Mauricio Daniel, B., Jose Misael, J. B. (2018), Precise weed and maize classification through convolutional neuronal networks. 2017 IEEE 2nd Ecuador Technical Chapters Meeting, ETCM 2017, January 2017, 1–6.
- [11] Lee, S. H., Chan, C. S., Mayo, S. J., Remagnino, P. (2017), How deep learning extracts and learns leaf features for plant classification. Pattern Recognition 71, 1–13.
- [12] Fan, Z., Lu, J., Gong, M., Xie, H., Goodman, E. D. (2018), Automatic tobacco plant detection in UAV images via deep neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(3), 876–887.
- [13] Kellenberger, B., Volpi, M., Tuia, D. (2017), Fast animal detection in UAV images using convolutional neural networks. International Geoscience and Remote Sensing Symposium (IGARSS), July 2017, 866–869, art. no 8127090.
- [14] Stojcsics D., Molnar A. (2018), Automated evaluation of agricultural damage using UAV survey. MACRo 2017: 6th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics, Târgu-Mureș, Romania, 27–28.10.2017, 40–46.
- [15] Vetrivel, A., Gerke, M., Kerle, N., Nex, F., Vosselman, G. (2018), Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning. ISPRS Journal of Photogrammetry and Remote Sensing 140, 45–59.
- [16] Qayyum, A., Malik, A. S., Saad, N. M., Iqbal, M., Faris Abdullah, M., Rasheed, W., Rashid Abdullah, T. A., Bin Jafaar, M. Y. (2017), Scene classification for aerial images based on CNN using sparse coding technique. International Journal of Remote Sensing 38(8–10), 2662–2685.
- [17] Sheppard, C., Rahnemoonfar, M. (2017), Real-time scene understanding for UAV imagery based on deep convolutional neural networks. International Geoscience and Remote Sensing Symposium (IGARSS), July 2017, 2243–2246, art. no 8127435.
- [18] Kertész, G., Szénási, S., Vámossy, Z. (2015), Parallelization methods of the template matching method on graphics accelerators. 16th IEEE International Symposium on Computational Intelligence and Informatics – CINTI, 161–164.
- [19] Lovas I., Molnár A. (2018), Orthophoto creation based on low resolution thermal aerial images. IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI 2018). Timișoara, Romania, 219–224.