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New building extraction method based on semantic segmentation

  • role： First author第一作者
  • Affiliation：

    Department of Electronic Engineering, Shantou University, Shantou 515063, China

  • Email：19lhlong@stu.edu.cn
  • Introduction：龙丽红，研究方向为图像目标检测、图像分割的理论和应用。E-mail： 19lhlong@stu.edu.cn
LONG Lihong1,  
  • Affiliation：

    School of Electronic and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China

ZHU Yuting2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Electronic Engineering, Shantou University, Shantou 515063, China

  • Email：jwyan@stu.edu.cn
  • Introduction：闫敬文，研究方向为遥感图像分析与识别、深度学习、小波分析。E-mail： jwyan@stu.edu.cn
YAN Jingwen1*,  
  • Affiliation：

    Department of Electronic Engineering, Shantou University, Shantou 515063, China

LIU Jingjin1,  
  • Affiliation：

    School of Computer Engineering, Jimei University, Xiamen 361021, China

WANG Zongyue3

Resümee

Semantic segmentation of high-resolution remote sensing image has important theoretical and practical value in the field of aerial image analysis. However, the traditional segmentation methods are prone to edge blur, loss of detail information, and low resolution due to the richness of building semantics and the complexity of image background in high-resolution remote sensing images.An end-to-end convolutional neural network called Dilated-UNet (D-UNet) is proposed to solve the problem of fuzzy boundary and information loss in high-resolution satellite image semantic segmentation. First, the U-Net network structure is improved and the multiscale dilated convolution module of four channels is expanded using the division technology. Each channel uses different convolution expansion rates to identify the multiscale semantic information for extracting richer detailed information. Second, a joint loss function of cross entropy and Dice coefficient is designed to achieve the desired segmentation effect.The model is comprehensively evaluated and tested on the Inria aerial image dataset. Experimental results show that the proposed remote sensing image segmentation method can effectively segment urban buildings at pixel level from high-resolution remote sensing images, and the segmentation accuracy is higher and is therefore better than those of other methods.Our proposed D-UNet can deliver automatic building segmentation from high-resolution remote sensing images with high accuracy. Thus, it is a useful tool for practical application scenarios.

Schlüsselwort

remote sensing images;semantic segmentation;Multiscale;dilated convolution;image processing

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