一项关于复杂形状的耕地分类的研究,考虑到多尺度的特征和边缘先验
Jianghui Xiao1, Dongmei Zhang2, Jiang Li3
1School of Computer Science, China University of Geosciences, 430074, Wuhan, Hubei, China.
Environmental monitoring and assessment
|August 15, 2024
概括
准确地绘制耕地地图对于农业至关重要. 一个新的网络,MFEPNet,有效地提取复杂形状的农田,克服了可变规模和模糊边缘的挑战,以改善农业发展.
科学领域:
- 农业遥感 农业遥感
- 地理空间分析是什么?
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 准确的耕地分布数据对于可持续农业至关重要.
- 现有的方法与碎片化,复杂形状的情节,可变的尺度和模糊的边缘作斗争.
- 卷积神经网络 (CNN) 模型在这种情况下难以提取上下文信息.
研究的目的:
- 提出一个新的网络,复杂形状的农田开采网络,考虑多尺度特征和边缘先验 (MFEPNet),以改善耕地开采.
- 解决现有模型在处理可变尺度,模糊边缘和农田图像中的有限全球背景方面的局限性.
主要方法:
- 开发了一个上下文交叉注意力融合模块,以使用CNN转换器架构集成本地和全球功能.
- 实现了多个尺度的特征重建模块,并基于信息的信息门,用于多个尺度的信息补偿.
- 设计了一个纹理增强的边缘模块,利用注意力机制来融合纹理,并重建了边缘改进的功能.
主要成果:
- MFEPNet有效地缓解了与可变尺度,模糊边缘和受限视野相关的问题.
- MFEPNet在常规农田数据集上获得了92.40%的平均交叉点,在不规则农田数据集上达到91.65%的平均交叉点.
- 超越了包括UNet,DeeplabV3+和PSPNet.net在内的已建立的深度学习模型.
结论:
- 与现有的基准模型相比,MFEPNet在提取复杂形状的耕地方面表现优异.
- 拟议的网络提供了一个可靠的解决方案,用于准确地绘制农田分布图,这对农业可持续性至关重要.
- 多尺度特征和边缘先验的集成显著提高了提取精度.
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