Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances
Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/16/7/1145 |
| _version_ | 1827286612104445952 |
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| author | Tianqi Zhao Yongcheng Wang Zheng Li Yunxiao Gao Chi Chen Hao Feng Zhikang Zhao |
| author_facet | Tianqi Zhao Yongcheng Wang Zheng Li Yunxiao Gao Chi Chen Hao Feng Zhikang Zhao |
| author_sort | Tianqi Zhao |
| collection | DOAJ |
| description | Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future. |
| first_indexed | 2024-04-24T10:35:12Z |
| format | Article |
| id | doaj.art-053cc128c5a848998618d5b55b532a7b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:35:12Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-053cc128c5a848998618d5b55b532a7b2024-04-12T13:25:26ZengMDPI AGRemote Sensing2072-42922024-03-01167114510.3390/rs16071145Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and AdvancesTianqi Zhao0Yongcheng Wang1Zheng Li2Yunxiao Gao3Chi Chen4Hao Feng5Zhikang Zhao6Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaShip detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.https://www.mdpi.com/2072-4292/16/7/1145ship detectiondeep learningoptical remote-sensing imagesconvolutional neural networktransformer |
| spellingShingle | Tianqi Zhao Yongcheng Wang Zheng Li Yunxiao Gao Chi Chen Hao Feng Zhikang Zhao Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances Remote Sensing ship detection deep learning optical remote-sensing images convolutional neural network transformer |
| title | Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances |
| title_full | Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances |
| title_fullStr | Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances |
| title_full_unstemmed | Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances |
| title_short | Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances |
| title_sort | ship detection with deep learning in optical remote sensing images a survey of challenges and advances |
| topic | ship detection deep learning optical remote-sensing images convolutional neural network transformer |
| url | https://www.mdpi.com/2072-4292/16/7/1145 |
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