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Sensors 2018, 18(11), 3799; https://doi.org/10.3390/s18113799

Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images

1
School of Electronics Engineering and Computer Science, Peking University, Beijing 100087, China
2
Beijing Key Laboratory of Embedded Real-Time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China
3
Department of Electronic and Information Engineering, North China University of Technology, Beijing 100144, China
*
Author to whom correspondence should be addressed.
Received: 12 September 2018 / Revised: 31 October 2018 / Accepted: 3 November 2018 / Published: 6 November 2018
(This article belongs to the Section Remote Sensors)
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Abstract

Due to strong ocean waves, broken clouds, and extensive cloud cover interferences, ocean ship detection performs poorly when using optical remote sensing images. In addition, it is a challenge to detect small ships on medium resolution optical remote sensing that cover a large area. In this paper, in order to balance the requirements of real-time processing and high accuracy detection, we proposed a novel ship detection framework based on locally oriented scene complexity analysis. First, the proposed method can separate a full image into two types of local scenes (i.e., simple or complex local scenes). Next, simple local scenes would utilize the fast saliency model (FSM) to rapidly complete candidate extraction, and for complex local scenes, the ship feature clustering model (SFCM) will be applied to achieve refined detection against severe background interferences. The FSM considers a fusion enhancement image as an input of the pulse response analysis in the frequency domain to achieve rapid ship detection in simple local scenes. Next, the SFCM builds the descriptive model of the ship feature clustering algorithm to ensure the detection performance on complex local scenes. Extensive experiments on SPOT-5 and GF-2 ocean optical remote sensing images show that the proposed ship detection framework has better performance than the state-of-the-art methods, and it addresses the tricky problem of real-time ocean ship detection under strong waves, broken clouds, extensive cloud cover, and ship fleet interferences. Finally, the proposed ocean ship detection framework is demonstrated on an onboard processing hardware. View Full-Text
Keywords: feature clustering; optical remote sensing; ship detection; scene partition; saliency feature clustering; optical remote sensing; ship detection; scene partition; saliency
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Zhuang, Y.; Qi, B.; Chen, H.; Bi, F.; Li, L.; Xie, Y. Locally Oriented Scene Complexity Analysis Real-Time Ocean Ship Detection from Optical Remote Sensing Images. Sensors 2018, 18, 3799.

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