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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2852-2856

Entropy enhanced unsupervised domain adaptive remote sensing image semantic segmentation

Zhang Xunhuia,b
Zhou Yonga,b
Zhao Jiaqia,b
Zhang Dia,b
Yao Ruia,b
Liu Binga,b
a. Engineering Research Center of Mine Digitization of Ministry of Education, b. School of Computer Science & Technology, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2020.11.0431

Abstract

In order to obtain a remote sensing image semantic segmentation model that could be used on unlabeled target data by using annotated source data, this paper proposed an end-to-end entropy-enhanced domain adaptive semantic segmentation method. Firstly, in order to make full use of the multi-scale information of remote sensing images and reduce the domain shift caused by sensor resolution between domains, the method used the atrous spatial pyramid pooling module as the classifier. Secondly, in order to correctly correspond to the unlabeled target domain categories, it used two classifiers for co-training. Then, it used the information entropy of the predicted value of the pixel as the weight of adversarial loss which was a measure of the confidence of the classification, so that the training could focus on the pixels that were difficult to classify and reduce the domain shift. Experiments on the ISPRS(WGII/4) 2D dataset, the mIoU of the proposed method is 18% and 12% higher than that of the direct use of segmentation model and the traditional adversarial method respectively. Experimental results show that the proposed method performs better than the direct use of segmentation model and traditional adversarial domain adaptive segmentation methods in remote sensing image domain adaptive semantic segmentation.

Foundation Support

国家自然科学基金资助项目(61806206,61772530)
江苏省自然科学基金资助项目(BK20180639,BK20201346)
江苏省六大高峰人才项目(2015-DZXX-010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.11.0431
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Technology of Graphic & Image
Pages: 2852-2856
Serial Number: 1001-3695(2021)09-054-2852-05

Publish History

[2021-09-05] Printed Article

Cite This Article

张勋晖, 周勇, 赵佳琦, 等. 基于熵增强的无监督域适应遥感图像语义分割 [J]. 计算机应用研究, 2021, 38 (9): 2852-2856. (Zhang Xunhui, Zhou Yong, Zhao Jiaqi, et al. Entropy enhanced unsupervised domain adaptive remote sensing image semantic segmentation [J]. Application Research of Computers, 2021, 38 (9): 2852-2856. )

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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