Inshore Ship Detection in Remote Sensing Images via Weighted Pose Voting

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Abstract
Inshore ship detection from high-resolution satellite images is a useful yet challenging task in remote surveillance and military reconnaissance. It is difficult to detect the inshore ships with high precision because various interferences are present in the harbor scene. An inshore ship detection method based on the weighted voting and rotation-scale-invariant pose is proposed to improve the detection performance. The proposed method defines the rotation angle pose and the scaling factor of the detected ship to detect the ship with different directions and different sizes. For each pixel on the ship template, the possible poses of a detection window are estimated according to all possible pose-related pixels. To improve robustness to the shape-similar distractor and various interferences, the score of the detection window is obtained by designing a pose weighted voting method. Moreover, the values of some parameters such as similarity threshold and the weight of “V” are investigated. The experimental results on actual satellite images demonstrate that the proposed method is invariant to rotation and scale and robust in the inshore ship detection. In addition, better detection performance is observed in comparison with the existing inshore ship detection algorithms in terms of precision rate and recall rate. The target pose of the detected ship can also be obtained as a byproduct of the ship detection.
Funding Information
  • National Natural Science Foundation of China, China (61373180, 61461047)
  • Technical Innovation Talent Project of Sichuan Province (2015042)

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