Identification of late rice farmland based on long-term remote sensing phenology information in Dongting Lake Plain

Hao Lin; Leishi Chen; Zhongshuai Zheng; Xiqiang Shuai; Shuai Jiang

doi:10.1117/12.3036496

16 August 2024 Identification of late rice farmland based on long-term remote sensing phenology information in Dongting Lake Plain

Hao Lin, Leishi Chen, Zhongshuai Zheng, Xiqiang Shuai, Shuai Jiang

Author Affiliations +

Proceedings Volume 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024); 132301N (2024) https://doi.org/10.1117/12.3036496
Event: Third International Conference on Machine Vision, Automatic Identification and Detection, 2024, Kunming, China

Abstract

In order to explore the extraction method of late rice planting areas in the Dongting Lake Plain represented by Junshan District, multispectral satellites and synthetic aperture radar satellites were combined to establish continuous remote sensing phenology curves for rice. By establishing multi-dimensional spatial features, based on an unsupervised classification spatial clustering algorithm, and using 2 years of continuous multispectral and SAR images, a complete continuous remote sensing phenology curve of rice was constructed by coupling, and accurate rice field plot information was screened. Therefore, the late rice phenological characteristic plot based on satellite images enables high-precision extraction of complex mountainous rice planting areas. The result shows, the method solved the adverse effects on rice remote sensing classification caused by problems such as broken underlying surface, scattered farmland distribution, and fewer available images in the study area. The overall classification accuracy is 86.49%, which is better than traditional supervised classification methods.

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

Citation Download Citation

Hao Lin, Leishi Chen, Zhongshuai Zheng, Xiqiang Shuai, and Shuai Jiang "Identification of late rice farmland based on long-term remote sensing phenology information in Dongting Lake Plain", Proc. SPIE 13230, Third International Conference on Machine Vision, Automatic Identification, and Detection (MVAID 2024), 132301N (16 August 2024); https://doi.org/10.1117/12.3036496

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KEYWORDS

Phenology

Remote sensing

Synthetic aperture radar

Image classification

Multispectral imaging

Satellites

Polarization

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