Ship detection in optical remote sensing images based on deep convolutional neural networks

Yuan Yao; Zhiguo Jiang; Haopeng Zhang; Danpei Zhao; Bowen Cai

doi:10.1117/1.JRS.11.042611

20 September 2017 Ship detection in optical remote sensing images based on deep convolutional neural networks

Yuan Yao, Zhiguo Jiang, Haopeng Zhang, Danpei Zhao, Bowen Cai

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 042611 (September 2017). https://doi.org/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.

Citation Download Citation

Yuan Yao, Zhiguo Jiang, Haopeng Zhang, Danpei Zhao, and 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). https://doi.org/10.1117/1.JRS.11.042611

Received: 22 April 2017; Accepted: 22 August 2017; Published: 20 September 2017

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