A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images
A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images
复制标题
一种利用高分辨率可见光遥感图像进行道路分割的 Y-Net 深度学习方法
DOI：
10.1080/2150704x.2018.1557791
复制
发表时间：
2019-04-03
影响因子：
2.3
通讯作者：
Jin, Shan
中科院分区：
文献类型：
--
作者：
Li, Ye;Xu, Lele;Jin, Shan
高分辨率可见光遥感影像道路分割为道路网自动生成提供了有效的途径。最近，基于卷积神经网络（CNN）的深度学习方法被广泛应用于道路分割。然而，这是一个挑战，大多数基于CNN的方法，以实现高分割精度时，处理高分辨率的可见光遥感图像与丰富的细节。为了解决这个问题，我们提出了一种基于Y形卷积网络（表示为Y-Net）的道路分割方法。Y-Net包含一个双臂特征提取模块和一个融合模块。特征提取模块包括用于语义特征的深度下采样到上采样子网络和用于细节特征的没有下采样的卷积子网络。融合模块将所有特征结合起来进行道路分割。利用该方案，Y网可以很好地从高分辨率图像中分割出多尺度道路（包括宽道路和窄道路）。在公共数据集和私有数据集上的测试和比较实验表明，Y-Net的分割精度高于其他四种最先进的方法，FCN（全卷积网络），U-Net，SegNet和FC-DenseNet（全卷积DenseNet）。特别是，Y-Net精确地分割了狭窄道路的轮廓，这是比较方法所遗漏的。
Road segmentation from high-resolution visible remote sensing images provides an effective way for automatic road network forming. Recently, deep learning methods based on convolutional neural networks (CNNs) are widely applied in road segmentation. However, it is a challenge for most CNN-based methods to achieve high segmentation accuracy when processing high-resolution visible remote sensing images with rich details. To handle this problem, we propose a road segmentation method based on a Y-shaped convolutional network (indicated as Y-Net). Y-Net contains a two-arm feature extraction module and a fusion module. The feature extraction module includes a deep downsampling-to-upsampling sub-network for semantic features and a convolutional sub-network without downsampling for detail features. The fusion module combines all features for road segmentation. Benefiting from this scheme, the Y-Net can well segment multi-scale roads (both wide and narrow roads) from high-resolution images. The testing and comparative experiments on a public dataset and a private dataset show that Y-Net has higher segmentation accuracy than four other state-of-art methods, FCN (Fully Convolutional Network), U-Net, SegNet, and FC-DenseNet (Fully Convolutional DenseNet). Especially, Y-Net accurately segments contours of narrow roads, which are missed by the comparative methods.