S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES
Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape fe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/423/2016/isprs-archives-XLI-B7-423-2016.pdf |
| _version_ | 1818084270643609600 |
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| author | R. Zhang J. Yao K. Zhang C. Feng J. Zhang |
| author_facet | R. Zhang J. Yao K. Zhang C. Feng J. Zhang |
| author_sort | R. Zhang |
| collection | DOAJ |
| description | Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution
remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works
mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more
automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN,
fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method.
Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from
the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out
by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals.
Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a
large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate
the efficiency and robustness of our proposed S-CNN-Based ship detector. |
| first_indexed | 2024-12-10T19:51:13Z |
| format | Article |
| id | doaj.art-1207f35e5fbd437aaf80ebaac6bace5e |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-10T19:51:13Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1207f35e5fbd437aaf80ebaac6bace5e2022-12-22T01:35:46ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B742343010.5194/isprs-archives-XLI-B7-423-2016S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGESR. Zhang0J. Yao1K. Zhang2C. Feng3J. Zhang4School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, ChinaChina Aerospace Science and Technology Corporation, Beijing City, ChinaReliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/423/2016/isprs-archives-XLI-B7-423-2016.pdf |
| spellingShingle | R. Zhang J. Yao K. Zhang C. Feng J. Zhang S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES |
| title_full | S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES |
| title_fullStr | S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES |
| title_full_unstemmed | S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES |
| title_short | S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES |
| title_sort | s cnn based ship detection from high resolution remote sensing images |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/423/2016/isprs-archives-XLI-B7-423-2016.pdf |
| work_keys_str_mv | AT rzhang scnnbasedshipdetectionfromhighresolutionremotesensingimages AT jyao scnnbasedshipdetectionfromhighresolutionremotesensingimages AT kzhang scnnbasedshipdetectionfromhighresolutionremotesensingimages AT cfeng scnnbasedshipdetectionfromhighresolutionremotesensingimages AT jzhang scnnbasedshipdetectionfromhighresolutionremotesensingimages |