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

Previous Articles    

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

RICH HTML

 11   

PDF (PC)

163

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
1 余帅, 汪西莉. 含多级通道注意力机制的CGAN遥感图像建筑物分割[J]. 中国图象图形学报, 2021, 26(3): 686-699.
Yu Shuai, Wang Xi-li. Remote sensing building segmentation by CGAN with multilevel channel attention mechanism[J]. Journal of Image and Graphics, 2021, 26(3): 686-699.
2 齐滨, 赵春晖, 王玉磊. 基于支持向量机与相关向量机的高光谱图像分类[J]. 吉林大学学报: 工学版, 2013, 43(S1): 143-147.
Qi Bin, Zhao Chun-hui, Wang Yu-lei. Hyperspectral imagery classification based on SVM and RVM[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(S1): 143-147.
3 Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4): 640-651.
4 Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation[C]∥International Conference on Medical image computing and computer-assisted intervention. Cham: Springer International Publishing, 2015: 234-241.
5 Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(12): 2481-2495.
6 何青, 孟洋洋, 李华智. 多层次编码—解码网络遥感图像建筑物分割[J]. 计算机应用研究, 2021, 38(8): 2510-2514.
He Qing, Meng Yang-yang, Li Hua-zhi. Multi-level encoding and decoding network remote sensing image building segmentation[J]. Application Research of Computers, 2021, 38(8): 2510-2514.
7 李传林, 黄风华, 胡威, 等. 基于Res_AttentionUnet的高分辨率遥感影像建筑物提取方法[J]. 地球信息科学学报, 2021, 23(12): 2232-2243.
Li Chuan-lin, Huang Feng-hua, Hu Wei, et al. Building extraction from high-resolution remote sensing image based on Res_AttentionUnet[J]. Journal of Geo-information Science, 2021, 23(12): 2232-2243.
8 杨潇宇, 汪西莉. 结合多尺度注意力和边缘监督的遥感图像建筑物分割模型[J]. 激光与光电子学进展, 2022, 59(22): 335-344.
Yang Xiao-yu, Wang Xi-li. Building segmentation model of remote sensing image combining multi-scale attention and edge supervision[J]. Laser & Optoelectronics Progress, 2022, 59(22): 335-344.
9 王舒洋, 慕晓冬, 杨东方, 等. 融合高阶信息的遥感影像建筑物自动提取[J]. 光学精密工程, 2019, 27(11): 2474-2483.
Wang Shu-yang, Mu Xiao-dong, Yang Dong-fang, et al. High-order statistics integration method for automatic building extraction of remote sensing images[J]. Optics and Precision Engineering, 2019, 27(11): 2474-2483.
10 Abdollahi A, Pradhan B, Alamri A M. An ensemble architecture of deep convolutional segnet and unet networks for building semantic segmentation from high-resolution aerial images[J]. Geocarto International, 2020, (3): 1-16.
11 Liu Y, Gross L, Li Z, et al. Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling[J]. IEEE Access, 2019, 7(1): 128774-128786.
12 Shunping J, Shiqing W, Meng L. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1): 574-586.
13 Mnih V. Machine Learning for Aerial Image Labeling[D]. Toronto: University of Toronto, 2013.
14 Hendrycks D, Gimpel K. Gaussian error linear units (gelus)[J/OL]. arXiv preprint arXiv: 1606. 08415, 2016.
15 He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-780.
16 Sandler M, Howard A, Zhu M, et al. Mobilenetv2: inverted residuals and linear bottlenecks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Piscataway, USA, 2018: 4510-4520.
17 Peng C, Zhang X, Yu G, et al. Large kernel matters-improve semantic segmentation by global convolutional network[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hawaii, USA, 2017: 4353-4361.
[1] Shan XUE,Ya-liang ZHANG,Qiong-ying LYU,Guo-hua CAO. Anti⁃unmanned aerial vehicle system object detection algorithm under complex background [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 891-901.
[2] Wei-xuan MA,Yan ZHANG,Chuan-xiang MA,Sa ZHU. Edge detection algorithm of noisy remote sensing image under different illumination conditions [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 241-247.
[3] Jun-jie WANG,Yuan-jun NONG,Li-te ZHANG,Pei-chen ZHAI. Visual relationship detection method based on construction scene [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 226-233.
[4] Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888.
[5] Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[6] Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880.
[7] Ming LIU,Yu-hang YANG,Song-lin ZOU,Zhi-cheng XIAO,Yong-gang ZHANG. Application of enhanced edge detection image algorithm in multi-book recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 891-896.
[8] Shi-min FANG. Multiple source data selective integration algorithm based on frequent pattern tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 885-890.
[9] Sheng-sheng WANG,Chen-xu LI,Xiang-yu WANG,Zhi-lin YAO,Yi-shen LIU,Jia-qian WU,Qing-ran YANG. Brain tumor image classification based on improved residual capsule network and sparrow search [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2653-2661.
[10] Xiang-jiu CHE,He-yuan CHEN. Muti⁃Object dishes detection algorithm based on improved YOLOv4 [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2662-2668.
[11] Sheng-sheng WANG,Jing-yu CHEN,Yi-nan LU. COVID⁃19 chest CT image segmentation based on federated learning and blockchain [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2164-2173.
[12] Hong-wei ZHAO,Zi-jian ZHANG,Jiao LI,Yuan ZHANG,Huang-shui HU,Xue-bai ZANG. Bi⁃direction segmented anti⁃collision algorithm based on query tree [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1830-1837.
[13] Jie CAO,Xue QU,Xiao-xu LI. Few⁃shot image classification method based on sliding feature vectors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1785-1791.
[14] Xiong-fei LI,Jia-jing WU,Xiao-li ZHANG,Ze-yu WANG,Yun-cong FENG. Remote sensing image fusion algorithm based on relative total variation structure extraction [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1775-1784.
[15] Chun-bo WANG,Xiao-qiang DI. Cloud storage integrity verification audit scheme based on label classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1364-1369.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd