Very deep learning for ship discrimination in Synthetic Aperture Radar imagery

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 2016-November, Issue , 2016, Pages 104-107

  Schwegmann, C P ^a,b   Kleynhans, W ^a,b   Salmon, B P ^c   Mdakane, L W ^a,b   Meyer, R G V ^a,b  

^a UNIVERSITY OF PRETORIA   (South Africa)

^b Future production Chemicals Cluster   (South Africa)

^c UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Machine learning; Marine technology; Synthetic aperture radar

Indexed keywords

EID: 85007489737     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2016.7729017     Document Type: Conference Paper

References (9)

1. C. Schwegmann, W. Kleynhans, and B. Salmon, "Manifold Adaptation for Constant False Alarm Rate Ship Detection in South African Oceans, " IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 7, pp. 1939-1404, July 2015.
2. J. R. A. Morillas, I. C. Garca, and U. Zolzer, "Ship detection based on SVM using color and texture features, " in Intelligent Computer Communication and Processing, 2015. ICCP 2015. IEEE International Conference on, Sept. 2015, pp. 343-350.
3. R. K. Srivastava, K. Greff, and J. Schmidhuber, "Training Very Deep Networks, " in 2015 Neural Information Processing Systems (NIPS 2015 Spotlight), Nov. 2015, pp. 1-11.
4. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition, " Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
5. A. Krizhevsky, "Learning multiple layers of features from tiny images, " Tech. Rep. Tech. Rep. 001, University of Toronto, Toronto, Canada, 2006.
6. K. El-Darymli, P. McGuire, D. Power, and C. Moloney, "Target detection in synthetic aperture radar imagery: A state-of-the-art survey, " Journal of Applied Remote Sensing, vol. 7, no. 1, pp. 071598-071598, Mar. 2013.
7. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, New York, NY, 2nd edition, 2001.
8. D. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization, " in 3rd International Conference on Learning Representations (ICLR2015), May 2015, pp. 1-11.
9. A. Krizhevsky, "Learning multiple layers of features from tiny images, " Tech. Rep. Tech. Rep. 001, University of Toronto, Toronto, Canada, 2006.