Comparison of Machine and Deep Learning Methods for Mapping Sea Farms Using High-Resolution Satellite Image
- Authors
- Choung, Yun-Jae; Jung, Donghwi
- Issue Date
- 2021
- Publisher
- COASTAL EDUCATION & RESEARCH FOUNDATION
- Keywords
- Machine learning; deep learning; sea farm; satellite image
- Citation
- JOURNAL OF COASTAL RESEARCH, v.114, no.sp1, pp.420 - 423
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF COASTAL RESEARCH
- Volume
- 114
- Number
- sp1
- Start Page
- 420
- End Page
- 423
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/139045
- DOI
- 10.2112/JCR-SI114-085.1
- ISSN
- 0749-0208
- Abstract
- 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.
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Collections - College of Engineering > School of Civil, Environmental and Architectural Engineering > 1. Journal Articles
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