Y. Liu, M. H. Zhang, P. Xu, and Z. W. Guo, SAR ship detection using sea-land segmentation-based convolutional 393 neural network, detection. International Workshop on Remote Sensing with Intelligent Processing, vol.396, p.16, 2017.

E. Khesali, H. Enayati, M. Modiri, and M. M. Aref, Automatic ship detection in Single-Pol SAR Images using 397 texture features in artificial neural networks. The International Archives of Photogrammetry, Remote Sensing 398 and Spatial Information Sciences, vol.40, p.17, 2015.

C. P. Schwegmann, W. Kleynhans, B. P. Salmon, L. W. Mdakane, and R. G. Meyer, Very deep learning for ship 400 discrimination in synthetic aperture radar imagery, IEEE International Geoscience and Remote Sensing 401 Symposium (IGARSS), pp.104-107, 2016.

L. Bedini, M. Righi, and E. Salerno, Size and Heading of SAR-Detected Ships through the Inertia Tensor

, Multidisciplinary Digital Publishing Institute Proceedings, vol.2, p.19, 2018.

M. Stasolla and H. Greidanus, The exploitation of Sentinel-1 images for vessel size estimation, Remote Sensing 405 Letters, vol.7, pp.1219-1228, 2016.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE 407 Conference on Computer Vision and Pattern Recognition, vol.408, p.21, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks

, Advances in neural information processing systems, pp.1097-1105, 2012.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter, Self-normalizing neural networks, Advances in, p.413

, Neural Information Processing Systems, vol.414, p.24, 2017.

M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, The importance of skip connections in 415 biomedical image segmentation, Deep Learning and Data Labeling

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, Deep learning classification of land cover and crop 418 types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, vol.14, p.26, 2017.

J. Geng, J. Fan, H. Wang, X. Ma, B. Li et al., High-resolution SAR image classification via deep 420 convolutional autoencoders, IEEE Geoscience and Remote Sensing Letters, vol.12, p.27, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, p.422

T. Tieleman and G. Hinton, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent 424 magnitude, vol.425, p.29, 2012.

L. Huang, B. Liu, B. Li, W. Guo, W. Yu et al., A dataset, p.426

, Sentinel-1 ship interpretation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.427, issue.2017, p.30

B. Kellenberger, D. Marcos, and D. Tuia, Detecting mammals in UAV images: Best practices to address a 429 substantially imbalanced dataset with deep learning. Remote Sensing of Environment, Chollet, F.; others. Keras, vol.216, p.32, 2015.

R. Kruse, C. Borgelt, F. Klawonn, C. Moewes, M. Steinbrecher et al., Multi-layer perceptrons, 432 Computational Intelligence

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and 434 semantic segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, vol.436, p.34, 2014.

P. Jaccard, The distribution of the flora in the alpine zone, New phytologist, vol.1, p.35, 1912.

J. Cohen, A coefficient of agreement for nominal scales, vol.438, pp.37-46, 1960.

K. Seungryong, B. Jeongju, and Y. Chan-su, Satellite image-based ship classification method with sentinel-1

, IW mode data, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.441-1300, 2019.

D. Power, J. Youden, K. Lane, C. Randell, and D. Flett, Iceberg detection capabilities of RADARSAT synthetic 443 aperture radar, Canadian Journal of Remote Sensing, vol.27, p.38, 2001.

C. Bentes, A. Frost, D. Velotto, and B. Tings, Ship-iceberg discrimination with convolutional neural networks