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| Content Provider | IET Digital Library |
|---|---|
| Author | Zhenzhen, Li Baojun, Zhao Linbo, Tang Zhen, Li Fan, Feng |
| Abstract | Ship classification in optical images has been challenged by the complexity of various ships, different imaging conditions, and limited labelled images. Traditional methods focus on extracting handcrafted features for classification, but often fails to design well-performed features for complex images. Here, the authors propose a ship classification approach with CNN. It is capable of learning discriminative features itself by supervised learning and achieving good classification performance. They build two small datasets of optical ship images for training and validation, and conduct several experiments. The experimental results indicate that their approach is effective for ship classification. |
| Starting Page | 7343 |
| Ending Page | 7346 |
| Page Count | 4 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 21, Nov (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/21 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0422 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-07-11 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Achieving Good Classification Performance Complex Image Computer Vision And Image Processing Technique Computing in Other Engineering Field Convolutional Neural Nets Convolutional Neural Network Different Imaging Condition Feature Extraction Handcrafted Feature Image Classification Image Recognition Learning in AI Limited Labelled Image Neural Computing Technique Optical Image Optical Ship Image Ship Classification Approach Ships Supervised Learning Traditional Method Focus |
| Content Type | Text |
| Resource Type | Article |
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