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

• Published in: Journal of applied remote sensing Vol. 11; no. 4; p. 042611
• Main Authors: Yao, Yuan, Jiang, Zhiguo, Zhang, Haopeng, Zhao, Danpei, Cai, Bowen
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.10.2017
• Subjects: convolutional neural networks; remote sensing images; ship detection; region proposal network
• ISSN: 1931-3195; 1931-3195
• DOI: 10.1117/1.JRS.11.042611

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.