The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Vessel detection and classification from spaceborne optical images: A literature survey
Open accessAbstract
This paper provides an overview of existing literature on vessel/ship detection and classification from optical satellite imagery. Although SAR (Synthetic Aperture Radar) is still the leading technology for maritime monitoring, the number of studies based on optical satellite data is quickly growing. Altogether we analysed 119 papers on optical vessel detection and classification for the period from 1978 to March 2017. We start by introducing all the existing sensor systems for vessel detection, but subsequently focus only on optical imaging satellites. The article demonstrates the temporal development of optical satellite characteristics and connects this to the number and frequency of publications on vessel detection. After presenting the methods used for optical imagery-based vessel detection and classification in detail, along with the achieved detection accuracies, we also report possibilities for fusing optical data with other data sources. The studied papers show that the most common factors greatly influencing the vessel detection accuracy are the following: different weather conditions affecting sea surface characteristics, the quantity of clouds and haze, solar angle, and imaging sensor characteristics. All these factors bring great variations in the selection of the most suitable method; some still continue to pose unsolved challenges. For higher relevance and wider usage, we suggest that the algorithms for detection and classification should support a variety of targets and meteorological conditions, and ideally also a variety of optical satellite sensors. At least, they should be tested on many images under different conditions. This is not usually the case in the existent literature. We also observed that many authors omit an appropriate performance quantification, which is critical for a practical assessment and a numerical comparison of the presented algorithms. Overall it can be seen that vessel monitoring from spaceborne optical images is a popular research topic and has a great operational potential in the near future due to the large amount of satellite data, much of it free and open.
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Cites
2015 IEEE International Conference on Image Processing (ICIP)
(2015) · doi:10.1109/icip30522.2015 · Cited by 717
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
Belward, A. S., & Skøien, J. O. (2015) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.03.009 · Cited by 440
Liu, Z., Wang, H., Weng, L., & Yang, Y. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2565705 · Cited by 439
Tang, J., Deng, C., Huang, G.-B., & Zhao, B. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2335751 · Cited by 407
Zhu, C., Zhou, H., Wang, R., & Guo, J. (2010) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2010.2046330 · Cited by 403
A comparison of three image-object methods for the multiscale analysis of landscape structure
Hay, G. J., Blaschke, T., Marceau, D. J., & Bouchard, A. (2003) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/s0924-2716(02)00162-4 · Cited by 393
Yu, L., & Gong, P. (2012) · International Journal of Remote Sensing · doi:10.1080/01431161.2011.636081 · Cited by 374
Geoscience and remote sensing symposium (IGARSS '88)
Milne, A. K. (1989) · Photogrammetria · doi:10.1016/0031-8663(89)90017-3 · Cited by 334
An automated object-based approach for the multiscale image segmentation of forest scenes
Hay, G. J., Castilla, G., Wulder, M. A., & Ruiz, J. R. (2005) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2005.06.005 · Cited by 319
Cited by
SmallSats: a new technological frontier in ecology and conservation?
Curnick, D. J., Davies, A. J., Duncan, C., Freeman, R., Jacoby, D. M. P., Shelley, H. T. E., Rossi, C., Wearn, O. R., Williamson, M. J., & Pettorelli, N. (2022) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.239 · Cited by 34
Metadata
- Authors
- Kanjir, U., Greidanus, H., & Oštir, K.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 207
- Pages
- 1-26
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W2786644902
- Cited by
- 326
- References
- 70
- FWCI
- 21.57
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Kanjir, U., Greidanus, H., & Oštir, K. (2018). Vessel detection and classification from spaceborne optical images: A literature survey. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.12.033
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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