Sugarcane classification using the Chinese satellite image with high-resolution based on the PSENet algorithm

Chen Chen; Xinyuan Gao; Linjiang Lou; Huibing Wang

doi:10.1117/12.3048932

27 November 2024 Sugarcane classification using the Chinese satellite image with high-resolution based on the PSENet algorithm

Chen Chen, Xinyuan Gao, Linjiang Lou, Huibing Wang

Author Affiliations +

Proceedings Volume 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024); 134021A (2024) https://doi.org/10.1117/12.3048932
Event: International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 2024, Zhengzhou, China

Abstract

Sugarcane is a major sugar crop which support for the half world sugar provide with widely growth in Guangxi, Guangdong, Yunnan, and Hainan of China. Remote sensing technology provide an effective way which is a large area coverage in short periods for sugarcane classification which is developed from MODIS data (250m) to submeter satellite data to more precise result. Deep learning with a high performance was used in recent years which needs high-quality samples. The number of samples with wildly distribution is one of important factors for the training result which needs heavy workflow of labeling by manual work. In this research, a residual neural network, PSENet is proposed to high accuracy of sugarcane classification compared with the U-Net Model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Chen Chen, Xinyuan Gao, Linjiang Lou, and Huibing Wang "Sugarcane classification using the Chinese satellite image with high-resolution based on the PSENet algorithm", Proc. SPIE 13402, International Conference on Remote Sensing, Mapping, and Geographic Information Systems (RSMG 2024), 134021A (27 November 2024); https://doi.org/10.1117/12.3048932

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KEYWORDS

Satellites

Data modeling

Feature extraction

Image classification

Performance modeling

Satellite imaging

Deep learning

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