Implementation of U-Net for Paddy Field Mapping Using Very High-Resolution Satellite Imagery
DOI:
https://doi.org/10.62146/ijecbe.v2i3.57Keywords:
Paddy Mapping, Remote Sensing, Pleiades, Very High Spatial Resolution, U-NetAbstract
Mapping rice fields using remote sensing is one method that can be used to determine the number of rice fields, especially in Indonesia. Using this method can increase effectiveness in agricultural resource management. This research uses Pleiades optical satellite image data with very high resolution which is capable of displaying data information on a larger scale. The rice field classification model in this study uses U-net to classifier between rice fields and non-rice fields. The performance of applying this model for the classification of paddy fields and non-rice fields is 96%. These results show that the U-net model is capable of classifying small rice fields with high accuracy
References
M. Wanget al. “Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy”. In: Agronomy 12.10 (2022). DOI: 10.3390/agronomy12102342.
X. Huang, B. Ziniti, and N. Torbick. “Communication assessing conflict driven food security in rakhine, myanmar with multisource imagery”. In: Land 8.6 (2019). DOI: 10.3390/LAND8060095.
U.Nation. Department of Economic and Social Affairs Sustainable Development. https://sdgs.un.org/goals. Accessed: 2024-07-06.
F. Waldner and F. I. Diakogiannis. “Deep learning on edge: Extracting field boundaries from satellite images with a convolutional neural network”. In: Remote Sens. Environ. 245.October 2019 (2020), p. 111741. DOI: 10.1016/j.rse.2020.111741.
Y. He et al. “Automated detection of boundary line in paddy field using MobileV2-UNet and RANSAC”. In: Comput. Electron. Agric. 194.December 2021 (2022), p. 106697. DOI: 10.1016/j.compag.2022.106697.
W.Song et al. “Hierarchical extraction of cropland boundaries using Sentinel-2 time series data in fragmented agricultural landscapes”. In: Comput. Electron. Agric. 212.August (2023), p. 108097. DOI: 10.1016/j.compag.2023.108097.
L. Xu et al. “Paddy rice mapping in thailand using time-series sentinel-1 data and deep learning model”. In: Remote Sens. 13.19 (2021). DOI: 10.3390/rs13193994.
R. Nurtyawan and N. Fiscarina. “Assessment of the Accuracy of Dem From Panchromatic Pleiades Imagery (Case Study: Bandung City. West Java)”. In: Int. J. Remote Sens. Earth Sci. 17.1 (2020), p. 34. DOI: 10.30536/j.ijreses.2020.v17.a3329.
European Space Agency. Pleiades. https://earth.esa.int/eogateway/missions/pleiades. Accessed: 2024-07-06.
Klatenkab.go.id. Pertanian dan Ketahanan Pangan. https://klatenkab.go.id. Accessed: 2024-07-06.
Satellite Imaging Corporation. Pleiades-1A Satellite Sensor (0.5m). https://www.satimagingcorp.com/satellite-sensors/pleiades-1/. Accessed: 2024-07-06.
C.Yanetal. “High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-TransUnet”. In: Comput. Electron. Agric. 210.November 2022 (2023), p. 107867. DOI: 10.1016/j.compag.2023.107867.
W.Xiao, S. Xu, and T. He. “Object-Based Algorithm — A Implementation in Hangjiahu Plain in China Using GEE Platform”. In: Remote Sens. 13 (2021), pp. 1–20.
N.Siddique et al. “U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications”. In: IEEE Access 9 (2021), pp. 82031–82057. DOI: 10.1109/ACCESS.2021.3086020.
I. Salehin and D. K. Kang. “A Review on Dropout Regularization Approaches for Deep Neural Net works within the Scholarly Domain”. In: Electron. 12.14 (2023). DOI: 10.3390/electronics12143106.
M.LiandA.Stein. “Mapping land use from high resolution satellite images by exploiting the spatial arrangement of land cover objects”. In: Remote Sens. 12.24 (2020), pp. 1–21. DOI: 10.3390/rs12244158.
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