| 摘要(英) |
Rice is one of Taiwan’s main crops. If we can obtain the farming region and area before its harvest, it will be able to estimate the consumption of water and the yield of rice, which will help the government to decide related strategies as soon as possible. In the early days, in order to obtain the fields of rice, the government would ask experts to mark the rice fields on aerial photographs. However, this method relies on human resources heavily, and the speed and accuracy of interpretation are obviously insufficient. In recent years, with the development of AI related technologies, using deep learning method to assist experts to do interpretation can not only greatly improve the efficiency of interpretation, but also reduce the misjudgments caused by human.
However, Semantic segmentation methods suitable for rice interpretation, such as deep learning models such as FCN or U-net, whose results are based on pixels, and each pixel exists independently, which is very easy to cause the salt and pepper effect, making the results difficult to be applied. Therefore, some post-processing methods are needed to convert the result into a shapefile to facilitate application. For this reason, we use the traditional region growing method assist semantic segmentation method to block rice mounds, which can compensate for the problem of salt and pepper effect. However, because the single threshold method of the traditional regional growth method is not flexible enough, it cannot cope with a few exceptions. Therefore, we propose to use siamese network as a rule in region growing method to improve the traditional regional growth method. |
| 參考文獻 |
[1] 李哲源，「區塊物件化分類模式於自動化製圖之研究-以水稻田坵塊主題圖為例」，逢甲5927學環境資訊科技研究所碩士論文，2010
[2] Lillesand, T. M. and Kiefer, R. W., 2000, Remote Sensing and Image Interpretation, 4thed., John Wiley & Sons, Inc.
[3] W.Weng andX.Zhu, “UNet: Convolutional Networks for Biomedical Image Segmentation,” IEEE Access, vol. 9, pp. 16591–16603, 2021, doi: 10.1109/ACCESS.2021.3053408
[4] J. Long, E. Shelhamer and T. Darrell, “Fully convolutional networks for semantic segmentation”, Proc. IEEE Conf. Comput. Vis. Pattern Recognition, 2015.
[5] Hojjatoleslami S. A. and Kittler J, “Region growing: A new approach”, IEEE, Image Processing, vol. 7, no. 7, pp. 1079-1084, 1998
[6] R. Adams and L. Bischof, "Seeded Region Growing", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 6, June 1994.
[7] .J. Bromley, I. Guyon, Y. LeCun, E. Sackinger, and R. Shah. “Signature verification using a siamese time delay neural network.” J. Cowan and G. Tesauro (eds) Advances in Neural Information Processing Systems, 1993.
[8] Gonzalez-Betancourt, M., Mayorga-Ruíz, L, “Normalized difference vegetation index for rice management in El Espinal, Colombia.”, Dyna 85 (205), 47–56, 2018
[9] Karen Simonyan, Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations (ICLR), 2015.
[10] Zhang, K., Ge, X., Shen, P., Li, W., Liu, X., Cao, Q., et al. “Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages.” Remote Sens. 11, 387. doi: 10.3390/rs11040387, 2019 |
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