A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images
AbstractWith the rapid development of optical remote sensing satellites, ship detection and identification based on large-scale remote sensing images has become a significant maritime research topic. Compared with traditional ocean-going vessel detection, inshore ship detection has received increasing attention in harbor dynamic surveillance and maritime management. However, because the harbor environment is complex, gray information and texture features between docked ships and their connected dock regions are indistinguishable, most of the popular detection methods are limited by their calculation efficiency and detection accuracy. In this paper, a novel hierarchical method that combines an efficient candidate scanning strategy and an accurate candidate identification mixture model is presented for inshore ship detection in complex harbor areas. First, in the candidate region extraction phase, an omnidirectional intersected two-dimension scanning (OITDS) strategy is designed to rapidly extract candidate regions from the land-water segmented images. In the candidate region identification phase, a decision mixture model (DMM) is proposed to identify real ships from candidate objects. Specifically, to improve the robustness regarding the diversity of ships, a deformable part model (DPM) was employed to train a key part sub-model and a whole ship sub-model. Furthermore, to improve the identification accuracy, a surrounding correlation context sub-model is built. Finally, to increase the accuracy of candidate region identification, these three sub-models are integrated into the proposed DMM. Experiments were performed on numerous large-scale harbor remote sensing images, and the results showed that the proposed method has high detection accuracy and rapid computational efficiency. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Bi, F.; Chen, J.; Zhuang, Y.; Bian, M.; Zhang, Q. A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images. Sensors 2017, 17, 1470.
Bi F, Chen J, Zhuang Y, Bian M, Zhang Q. A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images. Sensors. 2017; 17(7):1470.Chicago/Turabian Style
Bi, Fukun; Chen, Jing; Zhuang, Yin; Bian, Mingming; Zhang, Qingjun. 2017. "A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images." Sensors 17, no. 7: 1470.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.