The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images
Precisely monitoring the range of rice cultivation is an essential task for the government to dynamically supervise the red line of 180 million mu ($ 1 mu \approx 666.667 {m^2} $) of arable land. This study aims to address the issues of low efficiency, high cost, and insufficient accuracy in traditi...
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Surveying and Mapping Press
2024-12-01
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| Series: | Journal of Geodesy and Geoinformation Science |
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| Online Access: | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100194707-1106830193.pdf |
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| author | XU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen |
| author_facet | XU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen |
| author_sort | XU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen |
| collection | DOAJ |
| description | Precisely monitoring the range of rice cultivation is an essential task for the government to dynamically supervise the red line of 180 million mu ($ 1 mu \approx 666.667 {m^2} $) of arable land. This study aims to address the issues of low efficiency, high cost, and insufficient accuracy in traditional rice cultivation range monitoring methods. Against the backdrop of the widespread application of UAV remote sensing and the maturity of deep learning technology, this paper constructs a high-precision UAV remote sensing image dataset for rice identification, which includes different growth stages of rice, different resolutions, and regions. It also utilizes deep learning semantic segmentation technology to study the models, remote sensing image resolutions, and model sample sizes suitable for precise monitoring of rice. The experimental results show that, on the basis of balancing cost, efficiency, and accuracy, the Deeplabv3+ and PSPNet models combined with remote sensing image data of 8 cm resolution are more suitable for monitoring and extraction of rice cultivation areas, and PSPNet has a stronger few-shot learning ability. In response to the strong model generalization ability under the dispersed rice cultivation areas and diversified features, this paper proposes a method of transfer learning with a small number of samples. This method has a more stable training process, and the IoU is 5% $\sim$ 10% higher than that of unsupervised transfer learning models and fully supervised models with a small number of samples. |
| format | Article |
| id | doaj-art-6e87dd259e2a43d9aa601965b22cc954 |
| institution | Kabale University |
| issn | 2096-5990 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Surveying and Mapping Press |
| record_format | Article |
| series | Journal of Geodesy and Geoinformation Science |
| spelling | doaj-art-6e87dd259e2a43d9aa601965b22cc9542025-01-17T07:56:10ZengSurveying and Mapping PressJournal of Geodesy and Geoinformation Science2096-59902024-12-01749410910.11947/j.JGGS.2024.0407The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing ImagesXU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen01 Hainan Geomatics Center, Ministry of Natural Resources, Haikou 570203, China;2 School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;3 State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430071, ChinaPrecisely monitoring the range of rice cultivation is an essential task for the government to dynamically supervise the red line of 180 million mu ($ 1 mu \approx 666.667 {m^2} $) of arable land. This study aims to address the issues of low efficiency, high cost, and insufficient accuracy in traditional rice cultivation range monitoring methods. Against the backdrop of the widespread application of UAV remote sensing and the maturity of deep learning technology, this paper constructs a high-precision UAV remote sensing image dataset for rice identification, which includes different growth stages of rice, different resolutions, and regions. It also utilizes deep learning semantic segmentation technology to study the models, remote sensing image resolutions, and model sample sizes suitable for precise monitoring of rice. The experimental results show that, on the basis of balancing cost, efficiency, and accuracy, the Deeplabv3+ and PSPNet models combined with remote sensing image data of 8 cm resolution are more suitable for monitoring and extraction of rice cultivation areas, and PSPNet has a stronger few-shot learning ability. In response to the strong model generalization ability under the dispersed rice cultivation areas and diversified features, this paper proposes a method of transfer learning with a small number of samples. This method has a more stable training process, and the IoU is 5% $\sim$ 10% higher than that of unsupervised transfer learning models and fully supervised models with a small number of samples.http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100194707-1106830193.pdf|deep learning|rice recognition|transfer learning|sample library construction |
| spellingShingle | XU Chang, WANG Chunxiao, LIU Lu, YAN Xiaobin, LIU Xiaojuan, Chen Hui, CHENG Mingxing, FAN Yewen The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images Journal of Geodesy and Geoinformation Science |deep learning|rice recognition|transfer learning|sample library construction |
| title | The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images |
| title_full | The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images |
| title_fullStr | The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images |
| title_full_unstemmed | The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images |
| title_short | The Research on Precise Monitoring Methods for Grain Planting Areas Based on High-precision UAV Remote Sensing Images |
| title_sort | research on precise monitoring methods for grain planting areas based on high precision uav remote sensing images |
| topic | |deep learning|rice recognition|transfer learning|sample library construction |
| url | http://jggs.chinasmp.com/fileup/2096-5990/PDF/1737100194707-1106830193.pdf |
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