Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.6.1.7

The Optimal GSD and Image Size for Deep Learning Semantic Segmentation Training of Drone Images of Winter Vegetables  

Chung, Dongki (Department of Geoinformatics, University of Seoul/Innopam Co., Ltd)
Lee, Impyeong (Department of Geoinformatics, University of Seoul)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_1, 2021 , pp. 1573-1587 More about this Journal
Abstract
A Drone image is an ultra-high-resolution image that is several or tens of times higher in spatial resolution than a satellite or aerial image. Therefore, drone image-based remote sensing is different from traditional remote sensing in terms of the level of object to be extracted from the image and the amount of data to be processed. In addition, the optimal scale and size of data used for model training is different depending on the characteristics of the applied deep learning model. However, moststudies do not consider the size of the object to be found in the image, the spatial resolution of the image that reflects the scale, and in many cases, the data specification used in the model is applied as it is before. In this study, the effect ofspatial resolution and image size of drone image on the accuracy and training time of the semantic segmentation deep learning model of six wintering vegetables was quantitatively analyzed through experiments. As a result of the experiment, it was found that the average accuracy of dividing six wintering vegetablesincreases asthe spatial resolution increases, but the increase rate and convergence section are different for each crop, and there is a big difference in accuracy and time depending on the size of the image at the same resolution. In particular, it wasfound that the optimal resolution and image size were different from each crop. The research results can be utilized as data for getting the efficiency of drone images acquisition and production of training data when developing a winter vegetable segmentation model using drone images.
Keywords
Drone image; Semantic segmentation; Deeplabv3+; Winter vegetation; Spatial resolution;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Clack, W. and K. Avery, 1976. The effects of Data Aggregation in Statistical Analysis, Geographica Analysis, 8(4): 428-438.
2 Chen, L., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation, Proc. of the European conference on computer vision (ECCV), Munich, DE, Sep. 8-14, pp. 801-818.
3 Chen, L.C., G. Papandreou, I. Kokkinos, K. Murphy and A.L. Yuille, 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs, Proc. of In International Conference on Learning Representations, Banff, CAN, Apr. 14-16, arXiv preprint arXiv: 1412.7062.
4 Chiu, W.-T., C.-H. Lin, C.-L. Jhu, C. Lin, Y.-C. Chen, M.-J. Huang, 2020. Semantic Segmentation of Lotus Leaves in UAV Aerial Images via U-Net and DeepLab-based Networks, Proc. of International Computer Symposium (ICS), Tainan, TPE, Dec. 17-19, pp. 535-540.
5 Chew, R., J. Rineer, R. Beach, M. O'Neil, N. Ujeneza, D. Lapidus, and D.S. Temple, 2020. Deep Neural Networks and Transfer Learning for Food Crop Identification in UAV Images, Drones, 4(1): 7.   DOI
6 Tao, A., K. Sapra, and B. Catanzaro, 2020, Hierarchical multi-scale attention for semantic segmentation. arXiv preprint arXiv: 2005.10821.
7 Huang, L., X. Wu, Q. Peng, and X. Yu, 2021. Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains, Journal of Spectroscopy, 2021: 1-14.   DOI
8 Lee, S. and M. Lee, 2021. A Study of Establishment and application Algorithm of Artificial Intelligence Training Data on Land use/cover Using Aerial Photograph and Satellite Images, Korean Journal of Remote Sensing, 37(5-1): 871-884 (in Korean with English abstract).   DOI
9 Ku, C.Y., 2000. The scale characteristics of satellite imagery with spatial resolution, the instituts for Korean Regional Studies, Seoul, KOR (in Korean with English abstract).
10 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, 1(4): 541-551.   DOI
11 Ronneberger, O., P. Fischer, and T. Brox, 2015, U-net: Convolutional networks for biomedical image segmentation, Proc. of in International Conference on Medical image computing and computerassisted intervention, Springer, Cham, Oct. 5-9, pp. 234-241.
12 Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia, 2017, Pyramid scene parsing network, Proc. of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, Jul. 21-26, pp. 2881-2890.
13 Long, J., E. Shelhamer, and T. Darrell, 2015. Fully convolutional networks for semantic segmentation, Proc. of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, Jun. 7-12, pp. 3431-3440.