The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Ship Detection in Spaceborne Optical Image With SVD Networks
Abstract
Automatic ship detection on spaceborne optical images is a challenging task, which has attracted wide attention due to its extensive potential applications in maritime security and traffic control. Although some optical image ship detection methods have been proposed in recent years, there are still three obstacles in this task: 1) the inference of clouds and strong waves; 2) difficulties in detecting both inshore and offshore ships; and 3) high computational expenses. In this paper, we propose a novel ship detection method called SVD Networks (SVDNet), which is fast, robust, and structurally compact. SVDNet is designed based on the recent popular convolutional neural networks and the singular value decompensation algorithm. It provides a simple but efficient way to adaptively learn features from remote sensing images. We evaluate our method on some spaceborne optical images of GaoFen-1 and Venezuelan Remote Sensing Satellites. The experimental results demonstrate that our method achieves high detection robustness and a desirable time performance in response to all of the above three problems.
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Cited by
Object detection in optical remote sensing images: A survey and a new benchmark
Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
Deng, B. L., Li, G., Han, S., Shi, L., & Xie, Y. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2976475 · Cited by 996
A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines
Liu, Z., Yuan, L., Weng, L., & Yang, Y. (2017) · Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0006120603240331 · Cited by 686
Super-Resolution for Remote Sensing Images via Local–Global Combined Network
Lei, S., Shi, Z., & Zou, Z. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2704122 · Cited by 377
Self-citationRotated region based CNN for ship detection
Liu, Z., Hu, J., Weng, L., & Yang, Y. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8296411 · Cited by 258
Metadata
- Authors
- Zou, Z., & Shi, Z.
- Venue
- IEEE Transactions on Geoscience and Remote Sensing
- Published
- 2016-10
- Volume
- 54
- Issue
- 10
- Pages
- 5832-5845
- ISSN
- 0196-2892 (ISSN-L)
- 1558-0644
- OpenAlex
- W2442495293
- Cited by
- 257
- References
- 21
- FWCI
- 34.22
- Type
- journal article
- Language
- en
- Open access
- closed
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