Hedgerows are one of the few remaining natural landscape features within European agricultural areas. To facilitate hedgerow monitoring, cost-effective and accurate mapping of hedgerows across large spatial scales is required. Current methods used for automatic hedgerow detection are overly complicated and generalize poorly to larger areas. We examine the application of transfer learning using two neural networks (Mask R-CNN and DeepLab v3+) for hedgerow mapping in south-eastern Germany using IKONOS imagery. We demonstrate the potential of such networks for hedgerow monitoring by investigating performances across varying input image bands, seasonal imagery, and image augmentation strategies. Both networks successfully detected hedgerows across a large spatial scale (562 km2), with DeepLab v3+ (75% F1-score) outperforming Mask R-CNN. Differences between band combinations were minimal, implying hedgerow detection could be achieved using RGB sensors. Results suggested that using all available training images across seasons is preferred and should have the same model generalizing effects as data augmentation. Experiments with varying data augmentations found augmentations effecting object geometries to greatly increase performance for both networks while results using augmentations modifying pixel spectral values showed concerning effects. Overall, our study finds that transfer learning in neural networks offers a simplified approach that outperforms previously established methods.

中文翻译：

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使用卷积神经网络检测超高分辨率卫星图像中的树篱目标

绿篱是欧洲农业地区内仅有的少数自然景观之一。为了促进对树篱的监测，需要在大空间范围内经济高效且准确地绘制树篱的地图。用于自动树篱检测的当前方法过于复杂，并且推广到较大区域的效果很差。我们研究了使用两个神经网络（Mask R-CNN和DeepLab v3 +）进行迁移学习在德国东南部使用IKONOS影像进行树篱映射的应用。通过研究跨不同输入图像带，季节性图像和图像增强策略的性能，我们证明了这种网络可用于树篱监视的潜力。这两个网络都成功地在较大的空间范围（562 km2）上检测了篱笆，DeepLab v3 +（75％F1分数）的表现优于Mask R-CNN。波段组合之间的差异很小，这意味着可以使用RGB传感器实现树篱检测。结果表明，最好使用跨季节的所有可用训练图像，并且应该具有与数据增强相同的模型概括效果。使用各种数据增强的实验发现，增强会影响对象的几何形状，从而大大提高两个网络的性能，而使用增强修改像素光谱值的结果显示出与效果相关的效果。总体而言，我们的研究发现，神经网络中的转移学习提供了一种简化的方法，其性能优于先前建立的方法。结果表明，最好使用跨季节的所有可用训练图像，并且应该具有与数据增强相同的模型概括效果。使用各种数据增强的实验发现，增强会影响对象的几何形状，从而大大提高两个网络的性能，而使用增强修改像素光谱值的结果显示出与效果相关的效果。总体而言，我们的研究发现，神经网络中的转移学习提供了一种简化的方法，其性能优于先前建立的方法。结果表明，最好使用跨季节的所有可用训练图像，并且应该具有与数据增强相同的模型概括效果。使用各种数据增强的实验发现，增强会影响对象的几何形状，从而大大提高两个网络的性能，而使用增强修改像素光谱值的结果显示出与效果相关的效果。总体而言，我们的研究发现，神经网络中的转移学习提供了一种简化的方法，其性能优于先前建立的方法。