Exploring Data and Models in SAR Ship Image Captioning
In recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images; however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9868765/ |
| _version_ | 1818066227234340864 |
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| author | Kai Zhao Wei Xiong |
| author_facet | Kai Zhao Wei Xiong |
| author_sort | Kai Zhao |
| collection | DOAJ |
| description | In recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images; however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: first, to better describe SAR ship images, we propose certain principles for SAR image annotation based on the characteristics of SAR images. Second, to make better use of SAR ship images, a large-scale SAR ship image captioning dataset is carefully constructed. Finally, we explore encoder–decoder models and the attention mechanism and apply these methods to the SAR ship image captioning task. We conduct detailed experiments on the proposed dataset and find that the encoder–decoder model and attention mechanism can obtain good results in the SAR ship image captioning task. The experiments also reveal that the generated sentences can accurately describe SAR ship images. This dataset has already been published on <uri>https://github.com/5132210/SSIC.git</uri>. |
| first_indexed | 2024-12-10T15:04:26Z |
| format | Article |
| id | doaj.art-344486165be0472896507366175cb672 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T15:04:26Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-344486165be0472896507366175cb6722022-12-22T01:44:06ZengIEEEIEEE Access2169-35362022-01-0110911509115910.1109/ACCESS.2022.32021939868765Exploring Data and Models in SAR Ship Image CaptioningKai Zhao0Wei Xiong1Science and Technology on Complex Electronic System Simulation Laboratory, Space Engineering University, Beijing, ChinaScience and Technology on Complex Electronic System Simulation Laboratory, Space Engineering University, Beijing, ChinaIn recent years, considerable progress has been made in ship detection in synthetic aperture radar (SAR) images; however, no research has been conducted on translating SAR ship images into flexible and accurate sentences. To explore image captions in SAR ship images, we conduct the following work: first, to better describe SAR ship images, we propose certain principles for SAR image annotation based on the characteristics of SAR images. Second, to make better use of SAR ship images, a large-scale SAR ship image captioning dataset is carefully constructed. Finally, we explore encoder–decoder models and the attention mechanism and apply these methods to the SAR ship image captioning task. We conduct detailed experiments on the proposed dataset and find that the encoder–decoder model and attention mechanism can obtain good results in the SAR ship image captioning task. The experiments also reveal that the generated sentences can accurately describe SAR ship images. This dataset has already been published on <uri>https://github.com/5132210/SSIC.git</uri>.https://ieeexplore.ieee.org/document/9868765/SAR imageimage captioningencoder-decoderrecurrent neural networklong short-term memory network |
| spellingShingle | Kai Zhao Wei Xiong Exploring Data and Models in SAR Ship Image Captioning IEEE Access SAR image image captioning encoder-decoder recurrent neural network long short-term memory network |
| title | Exploring Data and Models in SAR Ship Image Captioning |
| title_full | Exploring Data and Models in SAR Ship Image Captioning |
| title_fullStr | Exploring Data and Models in SAR Ship Image Captioning |
| title_full_unstemmed | Exploring Data and Models in SAR Ship Image Captioning |
| title_short | Exploring Data and Models in SAR Ship Image Captioning |
| title_sort | exploring data and models in sar ship image captioning |
| topic | SAR image image captioning encoder-decoder recurrent neural network long short-term memory network |
| url | https://ieeexplore.ieee.org/document/9868765/ |
| work_keys_str_mv | AT kaizhao exploringdataandmodelsinsarshipimagecaptioning AT weixiong exploringdataandmodelsinsarshipimagecaptioning |