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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1572-1575,1580

Application of lightweight convolutional neural network in SAR image semantic segmentation

Shui Wenze1a
Sun Sheng1a
Yu Xu1b
Deng Shaoping2
1. a. School of Computer Science, b. School of Civil & Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. Zhongshan Basic Geographic Information Center, Zhongshan Guangdong 528400, China
DOI: 10.19734/j.issn.1001-3695.2020.05.0150

Abstract

This paper constructed a new TerraSAR-X dataset named GDUT-Nansha and then proposed a new lightweight semantic segmentation algorithm using synthetic aperture radar images(SAR). It is difficult to apply traditional deep learning models to SAR datasets with small volume due to the tremendous amount of parameters. A revision scheme is, therefore, put forward to deal with such problem. This paper accomplished an improved lightweight convolutional neural network model named revised weighted loss ENet(RWL-ENet) for SAR images. The current study introduced a weighted loss function to solve the problem of imbalance of training datasets. Compared with other classical convolution neural network models, the efficiency and robustness of the new dataset were validated. Meanwhile, RWL-ENet model attained 0.884, 0.804, and 0.645 in terms of three quantitative metrics, including pixel accuracy(PA), mean pixel accuracy(mPA), and mean intersection over union(mIoU). In addition, the parameters of this new proposed model are much less than other classic network models.

Foundation Support

国家自然科学基金资助项目(61672007)
广东省国际合作领域项目(2019A050509009)
广东省海洋与渔业厅渔港建设和渔业发展专项资助项目(A201701D04)
流域生态与地理环境监测国家局重点实验室资助项目(WE2016011)
自然资源部大湾区地理环境监测重点实验室开放基金资助项目(2019002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.05.0150
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Technology of Graphic & Image
Pages: 1572-1575,1580
Serial Number: 1001-3695(2021)05-055-1572-04

Publish History

[2021-05-05] Printed Article

Cite This Article

水文泽, 孙盛, 余旭, 等. 轻量化卷积神经网络在SAR图像语义分割中的应用 [J]. 计算机应用研究, 2021, 38 (5): 1572-1575,1580. (Shui Wenze, Sun Sheng, Yu Xu, et al. Application of lightweight convolutional neural network in SAR image semantic segmentation [J]. Application Research of Computers, 2021, 38 (5): 1572-1575,1580. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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