Comparison of Machine and Deep Learning Methods for Mapping Sea Farms Using High-Resolution Satellite Image

Yun Jae Choung, Donghwi Jung

Research output: Contribution to journalArticlepeer-review


Choung, Y.-J. and Jung, D. 2021. Comparison of machine and deep learning methods for mapping sea farms using high-resolution satellite image. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 420-423. Coconut Creek (Florida), ISSN 0749-0208. Previous research had shown that the supervised machine learning approach performed better than unsupervised machine learning for mapping sea farms using a high-resolution satellite image. The present work compares a support vector machine (SVM), which represents the supervised machine learning approach, and a deep neural network (DNN), which represents the deep learning approach, for mapping sea farms using KOMPSAT-3 satellite images acquired in the South Sea of South Korea. First, coastal maps were generated from the image source given by SVM and DNN. Next, the above-water and underwater farms were detected separately from both the maps based on the minimum and maximum thresholds. Finally, the detection accuracy of both the above-water and underwater farms from both coastal maps was assessed. Statistical results showed that deep learning (DNN) provided better performance than machine learning (SVM) for detecting above-water farms from the given high-resolution satellite image, while both DNN and SVM yielded the same performance for underwater farms. However, a few errors occurred in the detection because of the limitations of the pixel-based classification approaches. In future research, the deep learning algorithm combined with object-based classification, such as the convolutional neural network, can be used to detect sea farms from the given high-resolution image more accurately.

Original languageEnglish
Pages (from-to)420-423
Number of pages4
JournalJournal of Coastal Research
Issue numbersp1
Publication statusPublished - 2021 Oct 1

Bibliographical note

Funding Information:
This research was supported by the “Technology development for Practical Applications of Multi-Satellite data to maritime issues” funded by the Ministry of Ocean and Fisheries, Korea.

Publisher Copyright:
© 2021 Coastal Education Research Foundation Inc.. All rights reserved.


  • Machine learning
  • deep learning
  • satellite image
  • sea farm

ASJC Scopus subject areas

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes


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