我正在尝试使用 U-NET 架构对卫星图像进行分割,以识别已建成和未建成的区域。模型架构如下:
def get_unet(input_img, n_filters = 16, dropout = 0.1, batchnorm = True):
Function to define the UNET Model
# Contracting Path
c1 = conv2d_block(input_img, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)
p1 = MaxPooling2D((2, 2))(c1)
p1 = Dropout(dropout)(p1)
c2 = conv2d_block(p1, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)
p2 = MaxPooling2D((2, 2))(c2)
p2 = Dropout(dropout)(p2)
c3 = conv2d_block(p2, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)
p3 = MaxPooling2D((2, 2))(c3)
p3 = Dropout(dropout)(p3)
c4 = conv2d_block(p3, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)
p4 = MaxPooling2D((2, 2))(c4)
p4 = Dropout(dropout)(p4)
c5 = conv2d_block(p4, n_filters = n_filters * 16, kernel_size = 3, batchnorm = batchnorm)
# Expansive Path
u6 = Conv2DTranspose(n_filters * 8, (3, 3), strides = (2, 2), padding = 'same')(c5)
u6 = concatenate([u6, c4])
u6 = Dropout(dropout)(u6)
c6 = conv2d_block(u6, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)
u7 = Conv2DTranspose(n_filters * 4, (3, 3), strides = (2, 2), padding = 'same')(c6)
u7 = concatenate([u7, c3])
u7 = Dropout(dropout)(u7)
c7 = conv2d_block(u7, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)
u8 = Conv2DTranspose(n_filters * 2, (3, 3), strides = (2, 2), padding = 'same')(c7)
u8 = concatenate([u8, c2])
u8 = Dropout(dropout)(u8)
c8 = conv2d_block(u8, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)
u9 = Conv2DTranspose(n_filters * 1, (3, 3), strides = (2, 2), padding = 'same')(c8)
u9 = concatenate([u9, c1])
u9 = Dropout(dropout)(u9)
c9 = conv2d_block(u9, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)
outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)
model = Model(inputs=[input_img], outputs=[outputs])
return model
然而,我的分割掩码是嘈杂和像素化的,像这样:
此外,当我用这样的纯色示例图像测试我的模型时,它返回的结果是这样的。
U-Net 以不平滑的锯齿状方式遮罩。有什么解决办法吗?
提前致谢