Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery

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本文介绍了一种改进的U-Net网络，用于从卫星图像中精确提取道路。通过使用预训练的ResNet-34作为编码器，并结合Dropout和TTA技术，实现了对道路特征的有效检测。实验结果显示，该模型在Jaccard指数上表现优秀。

1.Introduction

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2. Model

我们对原始的U-Net网络进行了改进，使用了类似的网络和类似的编码器，使用预训练的ResNet-34网络作为编码器。卷积核的大小是7x7,stride=2,在每个residual block中，第一个卷积的步长是2，剩下的卷积操作步长都为1。
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3. Training

使用Jaccard index（IOU）作为评价指标
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离散公式：（分别为像素i的二值和预测概率）

损失函数(H:BCE)：

最小化L，相当于最小化H，并且最大化J
经过测试，α=0.7

使用Adam 优化器，IOU的预测值在迭代之间变化比较显著，使用了Dropout(0.3),使用了TTA（test time augmentation）,即将图像进行90度的旋转得到四张图像，预测然后取平均值。

References

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