Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2021 Mar 19;21(6):2153. doi: 10.3390/s21062153.
Roads are important mode of transportation, which are very convenient for people's daily work and life. However, it is challenging to accuratly extract road information from a high-resolution remote sensing image. This paper presents a road extraction method for remote sensing images with a complement UNet (C-UNet). C-UNet contains four modules. Firstly, the standard UNet is used to roughly extract road information from remote sensing images, getting the first segmentation result; secondly, a fixed threshold is utilized to erase partial extracted information; thirdly, a multi-scale dense dilated convolution UNet (MD-UNet) is introduced to discover the complement road areas in the erased masks, obtaining the second segmentation result; and, finally, we fuse the extraction results of the first and the third modules, getting the final segmentation results. Experimental results on the Massachusetts Road dataset indicate that our C-UNet gets the higher results than the state-of-the-art methods, demonstrating its effectiveness.
道路是重要的交通方式，非常方便人们的日常工作和生活。然而，从高分辨率遥感图像中准确提取道路信息具有挑战性。本文提出了一种基于补全 UNet（C-UNet）的遥感图像道路提取方法。C-UNet 包含四个模块。首先，标准 UNet 用于从遥感图像中粗略提取道路信息，得到第一个分割结果；其次，使用固定阈值擦除部分提取的信息；第三，引入多尺度密集扩张卷积 UNet（MD-UNet）来发现擦除掩模中的互补道路区域，得到第二个分割结果；最后，融合第一和第三模块的提取结果，得到最终的分割结果。在马萨诸塞州道路数据集上的实验结果表明，我们的 C-UNet 比最先进的方法获得了更高的结果，证明了其有效性。
