轻量级深度学习模型,ConvNeXt-U:改进的U-Net网络用于从Gaofen-2图像中提取复杂景观中的耕地
Shukuan Liu1, Shi Cao2, Xia Lu2
1School of Information Engineering, China University of Geosciences, Beijing 100083, China.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
一个新的深度学习模型,ConvNeXt-U,有效地从遥感数据中提取碎片化的耕地. 这种方法提高了准确性和速度,有助于可持续的农业管理.
科学领域:
- 遥感 遥感 遥感 遥感
- 农业科学 农业科学
- 计算机视觉 计算机视觉
背景情况:
- 分碎的耕地采掘对于可持续农业至关重要,但由于边界不规则和作物分布多样化,具有挑战性.
- 深度学习模型越来越多地用于土地覆盖分类,为复杂的开采任务提供潜在的解决方案.
研究的目的:
- 开发一个轻量级和高效的深度学习网络,用于提取分散的耕地.
- 从遥感图像中提高碎片化耕地开采的准确性和速度.
主要方法:
- 提出了ConvNeXt-U,这是一个新的网络,将简化的ConvNeXt编码器与U-Net解码器结合起来.
- 集成了轻量级的卷积块注意模块 (CBAM) 来增强特征表示.
- 使用来自中国恒阳县的GF-2遥感图像进行案例研究.
主要成果:
- ConvNeXt-U实现了高精度 (Acc = 85.2%,IoU = 79.5%),超过了Swin变压器,MobileNetV3,VGG16和ResUnet.net的性能.
- 与其他方法相比,证明了优越的推断速度 (37 图像/秒).
- 产生了更清晰,更完整的边界,用于碎片化的耕地开采.
结论:
- ConvNeXt-U模型显著提高了碎片化的耕地提取的准确性和效率.
- 集成ConvNeXt和CBAM模块有效地改善了边缘特征捕获和整体提取性能.
- ConvNeXt-U提供了一个具有成本效益和准确的解决方案,用于从遥感数据中提取碎片化的耕地.
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