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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 3
932-937

Depth convolution matching algorithm for multi-temporal remote sensing images

Wei Chuntao
Li Yuanzhe
School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0357

Abstract

Traditional manually designed feature descriptors are prone to cause a large number of mismatches in the face of multi temporal remote sensing images with large geometric and radiation differences and obvious changes in ground features. This paper developed a convolutional neural network matching algorithm with double screening and proposed double constraints to extract the deep semantic feature map of the image through CNN, it selected the key points that satisfied both the priority maximum principle and the precise extreme value principle from the feature map, and extracted 512-dimension descriptors from the corresponding positions. In the feature matching stage, the strategy of combining the rough to fine reverse matching constraints and RANSAC constraints ensured that the false matching points were eliminated effectively, and retained a certain number of correct matching points, which improved the matching accuracy. The experimental results show that the number of matches is moderate, the correct matching rate is improved compared with other algorithms selected in the experiment, and the image registration error is reduced by about 2 pixels. The running time is obviously improved, matching effect is good, with certain adaptability and robustness.

Foundation Support

重庆市基础科学与前沿技术研究专项重点项目(cstc2015jcyjBX0023)
桂林市科学技术研究开发项目(20190601)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0357
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 3
Section: Technology of Graphic & Image
Pages: 932-937
Serial Number: 1001-3695(2023)03-048-0932-06

Publish History

[2022-10-10] Accepted Paper
[2023-03-05] Printed Article

Cite This Article

韦春桃, 李渊哲. 多时相遥感影像的深度卷积匹配算法研究 [J]. 计算机应用研究, 2023, 40 (3): 932-937. (Wei Chuntao, Li Yuanzhe. Depth convolution matching algorithm for multi-temporal remote sensing images [J]. Application Research of Computers, 2023, 40 (3): 932-937. )

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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