A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images

Year 2020, Volume: 24 Issue: 1, 197 - 204, 01.02.2020

Ferhat Ucar , Deniz Korkmaz

https://doi.org/10.16984/saufenbilder.587731

Cited By: 2

Abstract

Ship target
classification from satellite images is a challenging task with its
requirements of feature extracting, advanced pre-processing, a variety of
parameters obtained from satellites and other type of images, and analyzing of
images. The dissimilarity of results, enhanced dataset requirement, intricacy
of the problem domain, general use of Synthetic Aperture Radar (SAR) images and
problems on generalizability are some topics of the issues related to ship
target detection. In this study, we propose a deep convolutional neural network
model for detecting the ships using the satellite images as inputs.  Our model has acquired an adequate accuracy
value by just using a pre-processed satellite image input. Visual and graphical
results of features at various layers and deconvolutions are also demonstrated
for a better understanding of the basic process.

Keywords

deep convolutional neural networks (CNNs), ship target classification, remote sensing, satellite imagery

References

• [1] Y. Liu, H.-Y. Cui, Z. Kuang, and G.-Q. Li, “Ship Detection and Classification on Optical Remote Sensing Images Using Deep Learning,” ITM Web Conf., vol. 12, p. 05012, 2017.
• [2] Z. Zou and Z. Shi, “Ship Detection in Spaceborne Optical Image with SVD Networks,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 10, pp. 5832–5845, 2016.
• [3] W. Liu, L. Ma, and H. Chen, “Arbitrary-Oriented Ship Detection Framework in Optical Remote-Sensing Images,” IEEE Geosci. Remote Sens. Lett., vol. 15, no. 6, pp. 937–941, 2018.
• [4] E. Ross, B. Arifin, and Y. Brodsky, “An information system for ship detection and identification,” Int. Geosci. Remote Sens. Symp., pp. 2081–2084, 2011.
• [5] U. Kanjir, H. Greidanus, and K. Oštir, “Vessel detection and classification from spaceborne optical images: A literature survey,” Remote Sens. Environ., vol. 207, no. November 2016, pp. 1–26, 2018.
• [6] S. Qi, J. Ma, J. Lin, Y. Li, and J. Tian, “Unsupervised Ship Detection Based on Saliency and S-HOG Descriptor From Optical Satellite Images,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 7, pp. 1451–1455, 2015.
• [7] H. Greidanus, M. Alvarez, C. Santamaria, F. X. Thoorens, N. Kourti, and P. Argentieri, “The SUMO ship detector algorithm for satellite radar images,” Remote Sens., vol. 9, no. 3, 2017.
• [8] F. Xu, J. Liu, M. Sun, D. Zeng, and X. Wang, “A hierarchical maritime target detection method for optical remote sensing imagery,” Remote Sens., vol. 9, no. 3, pp. 1–23, 2017.
• [9] Y. Yin, N. Liu, C. Li, W. Wan, and T. Fang, “Coarse-to-fine ship detection using visual saliency fusion and feature encoding for optical satellite images,” ICALIP 2016 - 2016 Int. Conf. Audio, Lang. Image Process. - Proc., pp. 705–710, 2017.
• [10] F. Yang, Q. Xu, and B. Li, “Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 5, pp. 602–606, 2017.
• [11] Zhenwei Shi, Xinran Yu, Zhiguo Jiang, and Bo Li, “Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature,” IEEE Trans. Geosci. Remote Sens., vol. 52, no. 8, pp. 4511–4523, 2013.
• [12] J. Tang, C. Deng, G. Bin Huang, and B. Zhao, “Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 3, pp. 1174–1185, 2015.
• [13] Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, Apr. 2016.
• [14] H. Lin, Z. Shi, and Z. Zou, “Fully Convolutional Network with Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images,” IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1665–1669, 2017.
• [15] R. Pradhan, R. S. Aygun, M. Maskey, R. Ramachandran, and D. J. Cecil, “Tropical Cyclone Intensity Estimation Using a Deep Convolutional Neural Network,” IEEE Trans. Image Process., vol. 27, no. 2, pp. 692–702, 2018.
• [16] M. H. Beale, M. T. Hagan, and H. B. Demuth, “Deep Learning Toolbox, User’s Guide.” Mathworks, 2018.
• [17] D. Moraite, “Satellite Images.” [Online]. Available: https://medium.com/dataseries/detecting-ships-in-satellite-imagery-7f0ca04e7964. [Accessed: 01-Jul-2019].