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Boundary Loss for Remote Sensing Imagery Semantic Segmentation

机译:遥感影像语义分割的边界损失

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In response to the growing importance of geospatial data, its analysis including semantic segmentation becomes an increasingly popular task in computer vision today. Convolutional neural networks are powerful visual models that yield hierarchies of features and practitioners widely use them to process remote sensing data. When performing remote sensing image segmentation, multiple instances of one class with precisely defined boundaries are often the case, and it is crucial to extract those boundaries accurately. The accuracy of segments boundaries delineation influences the quality of the whole segmented areas explicitly. However, widely-used segmentation loss functions such as BCE, IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently. fn this paper, we propose a novel loss function, namely a differentiable surrogate of a metric accounting accuracy of boundary detection. We can use the loss function with any neural network for binary segmentation. We performed validation of our loss function with various modifications of UNet on a synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL). Trained with the proposed loss function, models outperform baseline methods in terms of IoU score.
机译:为了响应地理空间数据的日益增长的重要性,包括语义分割在内的地理分析数据分析已成为当今计算机视觉中越来越流行的任务。卷积神经网络是功能强大的视觉模型,可产生功能层次结构,从业人员广泛使用它们来处理遥感数据。在执行遥感图像分割时,经常会遇到一类具有精确定义的边界的实例,因此准确提取这些边界至关重要。线段边界划定的准确性会明显影响整个线段区域的质量。但是,广泛使用的分段损失功能(例如BCE,IoU损失或Dice损失)不会充分惩罚边界的未对准。在本文中,我们提出了一种新颖的损失函数,即边界检测的度量记帐准确性的可微替代。我们可以将损失函数与任何神经网络一起使用来进行二进制分割。我们通过对合成数据集上的UNet进行各种修改以及使用真实数据(ISPRS Potsdam,INRIA AIL)对损失函数进行了验证。经过建议的损失函数训练后,模型在IoU评分方面优于基线方法。

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