NDLI: A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

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A Decision Mixture Model-Based Method for Inshore Ship Detection Using High-Resolution Remote Sensing Images

Content Provider	MDPI
Author	Bi, Fukun Chen, Jing Zhuang, Yin Bian, Mingming Zhang, Qingjun
Abstract	With 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.
File Size	3421184
File Format	PDF
e-ISSN	14248220
DOI	10.3390/s17071470
Journal	Sensors
Issue Number	7
Volume Number	17
Language	English
Publisher Date	2017-06-22
Access Restriction	Open
Subject Keyword	decision mixture model deformable part models (DPM) decision template ship detection remote sensing image
Content Type	Text
Resource Type	Article

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