Abstract
Monitoring sugarcane in China is important for the sugarcane industry which needs harvest progress information during the harvest season. The satellite image is one of the cost-effective and dynamic data sources for sugarcane classification recently. However, there are not many previous works for the classification of sugarcane with high-resolution satellite images especially sub-meter resolution data at present. Deep learning with a high performance of classification in agriculture was used in recent research. In this study, Chinese high-resolution satellite data based on the U-Net model was chosen to get a more precise segmentation of sugarcane in Laibin. GaoFen-1(GF-1) image with 2 m resolution and GaoFen-2(GF-2) image with 0.8 m resolution were compared. GF-2 image has a good performance in the OA and Kappa coefficient compared with the GF-1 image which shows that a high-resolution image can get better segmentation results of sugarcane than the low resolution using the same data and method. Furthermore, to get more precise results of sugarcane classification, two different growth stages of sugarcane GF-2 image were chosen: tillering period data in May and a grand growth period in August. The result shows the grand growth period is suitable for sugarcane classification with a better improvement in the OA and Kappa coefficient.
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Chen, C., Lou, L., Gao, X., Liu, Y. (2022). Comparative Analysis of Chinese High-Resolution Satellite Data for Sugarcane Classification Based on U-Net Model. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 7th China High Resolution Earth Observation Conference (CHREOC 2020). CHREOC 2020. Lecture Notes in Electrical Engineering, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-16-5735-1_14
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DOI: https://doi.org/10.1007/978-981-16-5735-1_14
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