20 September 2017 Ship detection in optical remote sensing images based on deep convolutional neural networks
Author Affiliations +
J. of Applied Remote Sensing, 11(4), 042611 (2017). doi:10.1117/1.JRS.11.042611
Abstract
Automatic ship detection in optical remote sensing images has attracted wide attention for its broad applications. Major challenges for this task include the interference of cloud, wave, wake, and the high computational expenses. We propose a fast and robust ship detection algorithm to solve these issues. The framework for ship detection is designed based on deep convolutional neural networks (CNNs), which provide the accurate locations of ship targets in an efficient way. First, the deep CNN is designed to extract features. Then, a region proposal network (RPN) is applied to discriminate ship targets and regress the detection bounding boxes, in which the anchors are designed by intrinsic shape of ship targets. Experimental results on numerous panchromatic images demonstrate that, in comparison with other state-of-the-art ship detection methods, our method is more efficient and achieves higher detection accuracy and more precise bounding boxes in different complex backgrounds.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yuan Yao, Zhiguo Jiang, Haopeng Zhang, Danpei Zhao, Bowen Cai, "Ship detection in optical remote sensing images based on deep convolutional neural networks," Journal of Applied Remote Sensing 11(4), 042611 (20 September 2017). http://dx.doi.org/10.1117/1.JRS.11.042611 Submission: Received 22 April 2017; Accepted 22 August 2017
Submission: Received 22 April 2017; Accepted 22 August 2017
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KEYWORDS
Remote sensing

Convolutional neural networks

Target detection

Clouds

Feature extraction

Image processing

Ocean optics

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