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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 11
3439-3445

Multi-label remote sensing image classification based on graph convolutional network

Yang Minhang1,2a,2b
Chen Long1,2a,2b
Liu Hui1,2b,2c
Qian Yurong1,2a,2b
1. Key Laboratory of Signal Detection & Processing, Xinjiang Uygur Autonomous Region, Urumqi 830046, China
2. a. College of Software, b. College of Information Science & Engineering, c. Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830000, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0153

Abstract

A single semantic category label cannot describe comprehensively the image content because remote sensing images contain various object categories. The task of multi-label image classification is more challenging. By exploring the depth graph convolutional network(GCN), this paper made up for the lack of relevance of label semantic information in multi-label remote sensing image classification, proposed a new multi-label remote sensing image classification network, multi-label remote sen-sing image classification network based on the GCN. It contained three parts: image feature learning module, classifiers lear-ning module based on GCN, and image feature differentiating module. Compared with the related models on the public multi label remote sensing datasets planet and UCM, the method can get better classification results on the multi label remote sensing image classification task. The method used modules such as graph convolution to apply multi-label image classification methods to remote sensing, which improved the model classification ability and shortened the model training time.

Foundation Support

国家自然科学基金资助项目(61966035)
智能多模态信息处理团队资助项目(XJEDU2017T002)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0153
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 11
Section: Technology of Graphic & Image
Pages: 3439-3445
Serial Number: 1001-3695(2021)11-042-3439-07

Publish History

[2021-11-05] Printed Article

Cite This Article

杨敏航, 陈龙, 刘慧, 等. 基于图卷积网络的多标签遥感图像分类 [J]. 计算机应用研究, 2021, 38 (11): 3439-3445. (Yang Minhang, Chen Long, Liu Hui, et al. Multi-label remote sensing image classification based on graph convolutional network [J]. Application Research of Computers, 2021, 38 (11): 3439-3445. )

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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