Abstract
Ship detection in remote sensing imagery has been widely applied in military and citizen applications, such as fishery management, vessel surveillance or marine safety and security. With the development of optical satellite, optical satellite imagery ship detection has caused a lot of attention. In this paper, we propose an offshore ship detection method based on sparse representation. First we employ histogram of oriented gradient (HOG) as the feature descriptor, then the HOG feature are extracted from training dataset. After feature extraction, all of samples are used to adaptively train a dictionary. Next, we encode HOG feature description of patches from test image by the dictionary. Finally, the sparse code and support vector machine (SVM) classification are employed in ship target validation and false alarms elimination. Experiments have shown better detection performance and stronger robustness of our method compared with other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhu, C., Zhou, H., Wang, R., et al.: A novel hierarchical method of ship detection from spaceborne optical image based on shape and texture features. IEEE Trans. Geosci. Remote Sens. 48(9), 3446–3456 (2010)
Bi, F., Zhu, B., Gao, L., et al.: A visual search inspired computational model for ship detection in optical satellite images. IEEE Geosci. Remote Sens. Lett. 9(4), 749–753 (2012)
Shi, Z., Yu, X., Jiang, Z., et al.: Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature. IEEE Trans. Geosci. Remote Sens. 52(8), 4511–4523 (2014)
Qi, S., Ma, J., Lin, J., et al.: Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images. IEEE Geosci. Remote Sens. Lett. 12(7), 1451–1455 (2015)
Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens. 54(10), 5832–5845 (2016)
Zhang, R., Yao, J., Zhang, K., et al.: S-CNN ship detection from high-resolution remote sensing images. In: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI-B7, pp. 423–430 (2016)
Yokoya, N., Iwasaki, A.: Object localization based on sparse representation for remote sensing imagery. In: Geoscience and Remote Sensing Symposium 2014, pp. 2293–2296. IEEE (2014)
Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40–44. IEEE (2002)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition, pp. 886–893. IEEE Computer Society (2005)
Mairal, J., Bach, F., Ponce, J., et al.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res.arch 11(1), 19–60 (2010)
Yang, G., Li, B., Ji, S.: Ship detection from optical satellite images based on sea surface analysis. IEEE Geosci. Remote Sens. Lett. 11(3), 641–645 (2014)
Acknowledgments
This work was supported in part by the Chang Jiang Scholars Program under Grant T2012122, in part by the Hundred Leading Talent Project of Beijing Science and Technology under Grant Z141101001514005, and in part by the National Natural Science Foundation of China under Grant 91438203.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, H., Zhuang, Y., Chen, L., Shi, H. (2018). Ship Detection in Optical Satellite Images Based on Sparse Representation. In: Sun, S., Chen, N., Tian, T. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2017. Lecture Notes in Electrical Engineering, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-10-7521-6_20
Download citation
DOI: https://doi.org/10.1007/978-981-10-7521-6_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7520-9
Online ISBN: 978-981-10-7521-6
eBook Packages: EngineeringEngineering (R0)