基于多尺度特征融合语义细分模型的高分辨率遥感图像中的作物分类
Tingyu Lu1, Meixiang Gao2,3, Lei Wang4
1College of Geographical Sciences, Harbin Normal University, Harbin, China.
Frontiers in plant science
|August 18, 2023
概括
本研究介绍了MSSNet,这是一个深度学习模型,用于使用遥感图像精确地绘制作物. 它有效地融合了多个尺度的特征,以提高分类准确性和详细的土地覆盖特征.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 农业科学 农业科学
背景情况:
- 深度学习在计算机视觉方面取得了巨大成功,为从遥感图像中智能提取信息提供了机会.
- 深度卷积神经网络在农业中越来越多地用于作物空间分布识别.
研究的目的:
- 为应对提高作物分类准确性和微粒度图像分类在遥感中的挑战.
- 提出一种新的多尺度特征融合语义细分模型 (MSSNet),用于增强作物识别.
主要方法:
- 作物映射是作为一个语义细分问题的框架.
- 拟议的MSSNet模型使用多分支非对称卷积和扩展卷积来进行多尺度特征提取.
- 功能通过连接融合,跳过连接整合浅层和深层网络功能以丰富语义信息.
主要成果:
- 使用Sentinel-2遥感图像的实验表明,MSSNet有效地利用了作物的光谱和空间特征.
- 该模型实现了良好的识别效果,改善了地图细分和地面物体的边缘特征.
- 农作物分类绘制输出显示在详细的土地覆盖范围划分方面表现优越.
结论:
- MSSNet模型为高精度作物绘图和田间地块提取提供了有价值的参考.
- 这种方法可以帮助减少农业遥感中的过度数据采集和处理要求.
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