结合GPS的SAT图道路识别

2019’ Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery

• PDF arxiv
• 代码 Github
• 数据未提供

基本介绍

• 网络结构：类UNet左右对称网络，主要设计体现在Decoder阶段上采样模块（文中提到：1D的上采样decoder模块，对于稀疏点的输入会比较鲁棒，Our 1D decoder has similar effect at roads with sparse samples, and they are not affected by sampling intervals.）

class DecoderBlock1DConv4(nn.Module):
    def __init__(self, in_channels, n_filters):
        super(DecoderBlock1DConv4, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1)
        self.norm1 = nn.BatchNorm2d(in_channels // 4)
        self.relu1 = nonlinearity

        self.deconv1 = nn.ConvTranspose2d(
            in_channels // 4, in_channels // 8, (1, 9), padding=(0, 4)
        )
        self.deconv2 = nn.ConvTranspose2d(
            in_channels // 4, in_channels // 8, (9, 1), padding=(4, 0)
        )
        self.deconv3 = nn.ConvTranspose2d(
            in_channels // 4, in_channels // 16, (9, 1), padding=(4, 0)
        )
        self.deconv4 = nn.ConvTranspose2d(
            in_channels // 4, in_channels // 16, (1, 9), padding=(0, 4)
        )

        self.norm2 = nn.BatchNorm2d(in_channels // 4 + in_channels // 8)
        self.relu2 = nonlinearity
        self.conv3 = nn.Conv2d(
            in_channels // 4 + in_channels // 8, n_filters, 1)
        self.norm3 = nn.BatchNorm2d(n_filters)
        self.relu3 = nonlinearity

    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.relu1(x)
        x1 = self.deconv1(x) # vertical 
        x2 = self.deconv2(x) # horizonal
        x3 = self.inv_h_transform(self.deconv3(self.h_transform(x))) # diag 
        x4 = self.inv_v_transform(self.deconv4(self.v_transform(x))) # anti-diag
        x = torch.cat((x1, x2, x3, x4), 1)

        x = F.interpolate(x, scale_factor=2)
        x = self.norm2(x)
        x = self.relu2(x)
        x = self.conv3(x)
        x = self.norm3(x)
        x = self.relu3(x)
        return x

    def h_transform(self, x):
        shape = x.size()
        x = torch.nn.functional.pad(x, (0, shape[-1]))
        x = x.reshape(shape[0], shape[1], -1)[..., :-shape[-1]]
        x = x.reshape(shape[0