Format

Send to

Choose Destination
Sensors (Basel). 2019 Feb 7;19(3). pii: E684. doi: 10.3390/s19030684.

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning.

Gao L1, He Y2,3, Sun X4,5, Jia X6, Zhang B7,8.

Author information

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. gaolr@radi.ac.cn.
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. heyq@radi.ac.cn.
3
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. heyq@radi.ac.cn.
4
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. sunxu@radi.ac.cn.
5
CETC Key Laboratory of Aerospace Information Applications, Shijiazhuang 050081, China. sunxu@radi.ac.cn.
6
School of Engineering and Information Technology, The University of New South Wales, Canberra Campus, Canberra, ACT 2006, Australia. x.jia@adfa.edu.au.
7
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. zb@radi.ac.cn.
8
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China. zb@radi.ac.cn.

Abstract

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields-including maritime traffic survey and maritime rescue-it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea⁻land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea⁻land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

KEYWORDS:

deep learning; high-resolution satellite images; negative sample training; sea–land segmentation; ship detection

PMID:
30736485
DOI:
10.3390/s19030684
Free full text

Supplemental Content

Full text links

Icon for Multidisciplinary Digital Publishing Institute (MDPI)
Loading ...
Support Center