Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 248-254.doi: 10.13229/j.cnki.jdxbgxb20211389

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Building segmentation method of remote sensing image based on improved SegNet

Bing ZHU1(),Zi-wei LI2,Qi LI1,3()   

  1. 1.College of Computer Science and Technology，Changchun University of Science and Technology，Changchun 130022，China
    2.College of Computer，Jilin Normal University，Siping 136000，China
    3.Zhongshan Institute，Changchun University of Science and Technology，Zhongshan 528437，China
  • Received:2021-12-16 Online:2023-01-01 Published:2023-07-23
  • Contact: Qi LI E-mail:baulina@163.com;liqi@cust.edu.cn

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Abstract:

Aiming at the low accuracy of building segmentation and boundary blur in high resolution remote sensing images， an improved full convolution network is proposed based on SegNet network. Firstly， GELU， which performs well in deep learning task， is selected as the activation function to avoid neuron inactivation； Secondly， the improved residual bottleneck structure is used to extract more building features in the encoding network； Then， the skip connection is used to fuse the low-level and high-level semantic features of the image to assist image reconstruction； Finally， an improved boundary refinement module is connected at the end of the decoding network to further correct the boundary details of the building and improve the boundary integrity of the building. The experiment is carried out on the Massachusetts Buildings Dataset. The accuracy， recall and F1 score reach 93.5%， 79.3% and 81.9% respectively. The F1 score of the comprehensive evaluation index is about 5% higher than that of the basic network.

Key words: computer application technology, remote sensing images, building segmentation, SegNet, full convolution network

CLC Number: 

  • TP389.1

Fig.1

RsBR-SegNet model diagram"

Fig.2

GELU activation function image"

Fig.3

Improved residual bottleneck structure diagram"

Fig.4

Comparison diagram of boundary refinement model"

Fig.5

Comparison diagram of satellite dataset I （global cities） segmentation results"

Table 1

Evaluation of Satellite dataset I （global cities）"

网络名称精确率召回率F1值交并比
FCN0.863 150.692 520.725 430.575 86
SegNet0.839 970.620 440.666 170.506 69
U?Net0.866 160.731 940.731 960.598 67
RsBR?SegNet0.875 260.754 580.759 070.618 34

Fig.6

Comparison diagram of Massachusetts Buildings Dataset segmentation results"

Table 2

Evaluation of Massachusetts Buildings Dataset"

网络名称精确率召回率F1值交并比
FCN0.917 890.738 960.771 180.630 75
SegNet0.918 440.731 450.770 000.629 98
U?Net0.923 450.758 020.793 030.674 29
RsBR?SegNet0.935 030.792 880.819 150.697 46
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