基于U-Net卷积神经网络的农业塑料温室的提取,加上边缘扩张和损失功能的改进
Weidong Song1, Huan He1, Jiguang Dai1
1College of Surveying and Geographic Sciences, School of Geomatics and Geographic Information Science, Liaoning Technical University, Fuxin, People's Republic of China.
Journal of the Air & Waste Management Association (1995)
|October 23, 2024
概括
这项研究通过使用改进的U-Net模型与遥感数据来增强农业塑料温室 (APG) 地图. 新方法提高了准确性,以改善农业管理和可持续实践.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 农业科学 农业科学
背景情况:
- 农业塑料温室 (APG) 的精确空间分布对于可持续农业至关重要.
- 传统的APG识别方法是劳动密集型的,缺乏可重复性.
- 与传统方法相比,深度学习可以从遥感图像中提取优越的特征.
研究的目的:
- 使用遥感数据提高农业塑料温室 (APG) 提取的准确性.
- 开发一种快速而精确的方法来绘制APG位置和数量.
- 通过更好的APG库存来加强农业管理和环境监测.
主要方法:
- 使用GF-7卫星图像进行APG检测.
- 开发了一个增强的U-Net卷积神经网络 (CNN) 模型.
- 嵌入边缘信息扩展 (卡尼运算符和高斯核) 和联合损失函数 (二进制交叉和GK函数).
主要成果:
- 增强的U-Net模型仅用边缘扩展就提高了1.1%的APG提取精度.
- 结合边缘扩张和关节损失约束,进一步提高了1.9%的精度.
- 经过修改的U-Net模型显示了比传统方法更高的提取精度.
结论:
- 增强的U-Net模型为绘制农业塑料温室 (APG) 提供了更准确,更有效的方法.
- 这一进步支持农民优化资源管理,并促进可持续的农业实践.
- 改善APG分布的监测对于有效的农业规划和环境监督至关重要.
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