skip to main content
10.1145/3502827.3502830acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicaipConference Proceedingsconference-collections
research-article

Study on Automatic Shoreline Extraction Based on Multi-spectral Remote Sensing Images

Published:27 January 2022Publication History

ABSTRACT

Remote sensing images contain important scientific data reflecting earth resources. Designing a computer algorithm to extract shoreline information from remote sensing images quickly and accurately is an important research direction in ocean engineering. In this paper, through digital image processing technology, edge detection and threshold segmentation algorithm, based on multi-spectral remote sensing images, the computer algorithm is designed and programmed. The shoreline in remote sensing images is extracted, and the accuracy is evaluated. The results show that the automatic shoreline extraction method proposed in this paper has high accuracy and practical application value.

References

  1. Yasir Muhammad, shoreline extraction and land use change analysis using remote sensing (RS) and geographic information system (GIS) technology - A review of the literature.. Reviews on environmental health .(2020): doi:10.1515/reveh-2019-0103.Google ScholarGoogle Scholar
  2. Chao Huang, High-Efficiency Determination of shoreline by Combination of Tidal Level and Coastal Zone DEM from UAV Tilt Photogrammetry. Remote Sensing 12.14(2020): doi:10.3390/rs12142189.Google ScholarGoogle Scholar
  3. Ozan Arslan, Dimension reduction methods applied to shoreline extraction on hyperspectral imagery.  Geocarto International 35.4(2020): doi:10.1080/10106049.2018.1520920.Google ScholarGoogle Scholar
  4. Ting Yang, Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery.  Marine Geodesy 43.2(2020): doi:10.1080/01490419.2020.1713266.Google ScholarGoogle Scholar
  5. Sara Zollini, Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy.  Journal of Marine Science and Engineering 8.1(2019): doi:10.3390/jmse8010009.Google ScholarGoogle Scholar
  6. Hossain, Md Sakaouth, Automatic shoreline extraction and change detection: A study on the southeast coast of Bangladesh. Marine Geology 441 (2021): 106628.Google ScholarGoogle ScholarCross RefCross Ref
  7. Zhu, Shiping, An image segmentation algorithm in image processing based on threshold segmentation. 2007 third international IEEE conference on signal-image technologies and internet-based system. IEEE, 2007.Google ScholarGoogle Scholar
  8. Ziou, Djemel, and Salvatore Tabbone. Edge detection techniques-an overview. Pattern Recognition and Image Analysis C/C of Raspoznavaniye Obrazov I Analiz Izobrazhenii 8 (1998): 537-559.Google ScholarGoogle Scholar
  9. Yang, Lei, An improved Prewitt algorithm for edge detection based on noised image. 2011 4th International congress on image and signal processing. Vol. 3. IEEE, 2011.Google ScholarGoogle Scholar
  10. Tang, Jiali, Image edge detection based on singular value feature vector and gradient operator. Math. Biosci. Eng 17.4 (2020): 3721-3735.Google ScholarGoogle ScholarCross RefCross Ref
  11. Wu, Fangsheng, Research on image text recognition based on canny edge detection algorithm and k-means algorithm. International Journal of System Assurance Engineering and Management (2021): 1-9.Google ScholarGoogle Scholar
  12. Zhu, Qidan, Liqiu Jing, and Rongsheng Bi. Exploration and improvement of Ostu threshold segmentation algorithm. 2010 8th World Congress on Intelligent Control and Automation. IEEE, 2010.Google ScholarGoogle Scholar
  13. Duan, Rui-ling, Qing-xiang Li, and Yu-he Li. Summary of image edge detection. Optical Technique 3.3 (2005): 415-419.Google ScholarGoogle Scholar
  14. Di, Kaichang, Automatic shoreline extraction from high-resolution IKONOS satellite imagery. Proceeding of ASPRS 2003 Annual Conference. Vol. 3. 2003.Google ScholarGoogle Scholar
  15. Ryan, T. W., Extraction of shoreline features by neural nets and image processing. Photogrammetric Engineering and Remote Sensing 57.7 (1991): 947-955.Google ScholarGoogle Scholar

Index Terms

  1. Study on Automatic Shoreline Extraction Based on Multi-spectral Remote Sensing Images
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Other conferences
            ICAIP '21: Proceedings of the 5th International Conference on Advances in Image Processing
            November 2021
            112 pages
            ISBN:9781450385183
            DOI:10.1145/3502827

            Copyright © 2021 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 27 January 2022

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article
            • Research
            • Refereed limited
          • Article Metrics

            • Downloads (Last 12 months)11
            • Downloads (Last 6 weeks)0

            Other Metrics

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format .

          View HTML Format