Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models
The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/6 |
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| _version_ | 1832588275144982528 |
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| author | Dongmin Seo Daekyeom Lee Sekil Park Sangwoo Oh |
| author_facet | Dongmin Seo Daekyeom Lee Sekil Park Sangwoo Oh |
| author_sort | Dongmin Seo |
| collection | DOAJ |
| description | The identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical and chemical properties of target objects. This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. Hyperspectral data from six ports in South Korea were collected using airborne sensors and subsequently processed into spectral statistics and RGB images. The processed data were then analyzed using classifier and convolutional neural network (CNN) models. The results obtained in this study show that CNN models achieved an average test accuracy of 90%, outperforming classifier models, which achieved 83%. Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. This study presents a robust and efficient framework for marine surveillance utilizing hyperspectral imaging and machine learning, offering significant potential for advancing marine detection and monitoring technologies. |
| format | Article |
| id | doaj-art-5b735ba7f016472084e29c807d9ab6fa |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-5b735ba7f016472084e29c807d9ab6fa2025-01-24T13:36:31ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-01131610.3390/jmse13010006Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier ModelsDongmin Seo0Daekyeom Lee1Sekil Park2Sangwoo Oh3Department of Electrical and Electronic Engineering, Semyung University, Jecheon 27136, Republic of KoreaSEASON Co., Ltd., Sejong City 30127, Republic of KoreaMaritime Digital Transformation Research Center, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of KoreaOcean and Maritime Digital Technology Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of KoreaThe identification of maritime objects is crucial for ensuring navigational safety, enabling effective environmental monitoring, and facilitating efficient maritime search and rescue operations. Given its ability to provide detailed spectral information, hyperspectral imaging has emerged as a powerful tool for analyzing the physical and chemical properties of target objects. This study proposes a novel maritime object identification framework that integrates hyperspectral imaging with machine learning models. Hyperspectral data from six ports in South Korea were collected using airborne sensors and subsequently processed into spectral statistics and RGB images. The processed data were then analyzed using classifier and convolutional neural network (CNN) models. The results obtained in this study show that CNN models achieved an average test accuracy of 90%, outperforming classifier models, which achieved 83%. Among the CNN models, EfficientNet B0 and Inception V3 demonstrated the best performance, with Inception V3 achieving a category-specific accuracy of 97% when weights were excluded. This study presents a robust and efficient framework for marine surveillance utilizing hyperspectral imaging and machine learning, offering significant potential for advancing marine detection and monitoring technologies.https://www.mdpi.com/2077-1312/13/1/6hyperspectral imagingmaritime object identificationdeep learningmachine learningconvolutional neural networksclassifier models |
| spellingShingle | Dongmin Seo Daekyeom Lee Sekil Park Sangwoo Oh Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models Journal of Marine Science and Engineering hyperspectral imaging maritime object identification deep learning machine learning convolutional neural networks classifier models |
| title | Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models |
| title_full | Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models |
| title_fullStr | Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models |
| title_full_unstemmed | Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models |
| title_short | Hyperspectral Image-Based Identification of Maritime Objects Using Convolutional Neural Networks and Classifier Models |
| title_sort | hyperspectral image based identification of maritime objects using convolutional neural networks and classifier models |
| topic | hyperspectral imaging maritime object identification deep learning machine learning convolutional neural networks classifier models |
| url | https://www.mdpi.com/2077-1312/13/1/6 |
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