1 |
Kang, M., K. Ji, X. Leng, and Z. Lin, 2017. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection, Remote Sensing, 9(8): 860.
DOI
|
2 |
Kim, S.W., D.H. Kim, and Y.K. Lee, 2018. Operational ship monitoring based on integrated analysis of KOMPSAT-5 SAR and AIS data, Korean Journal of Remote Sensing, 34(2-2): 327-338 (in Korean with English abstract).
DOI
|
3 |
Jiao, J., Y. Zhang, H. Sun, X. Yang, X. Gao, W. Hong, K. Fu, and X. Sun, 2018. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection, IEEE Access, 6: 20881-20892.
DOI
|
4 |
Paek, S. W., S. Balasubramanian, S. Kim, and O. de Weck, 2020. Small-satellite synthetic aperture radar for continuous global biospheric monitoring: A review, Remote Sensing, 12(16): 2546.
DOI
|
5 |
Lee, K.J., K.Y. Oh, and T.B. Chae, 2019. Development and Application Status of Microsatellites, Current Industrial and Technological Trends in Aerospace, 17(2): 113-124 (in Korean with English abstract).
|
6 |
Lee, S.J., T.B. Chae, and K.T. Kim, 2018. Analysis of Ship Classification Performances Using Open SARShip DB, Korean Journal of Remote Sensing, 34(5): 801-81 (in Korean with English abstract).
DOI
|
7 |
Nanosat Database. https://www.nanosats.eu/, Accessedon Jun. 22, 2021.
|
8 |
Qi, S., J. Ma, J. Lin, Y. Li, and J. Tian, 2015. Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images, IEEE Geoscience and Remote Sensing Letters, 12(7): 1451-1455.
DOI
|
9 |
Sharifzadeh, F., G. Akbarizadeh, and Y.S. Kavian, 2019. Ship classification in SAR images using a new hybrid CNN-MLP classifier, Journal of the Indian Society of Remote Sensing, 47(4): 551-562.
DOI
|
10 |
Song, J., D.J. Kim, and K.M. Kang, 2020. Automated procurement of training data for machine learning algorithm on ship detection using AIS information, Remote Sensing, 12(9): 1443.
DOI
|
11 |
Lei, F., W. Wang, and W. Zhang, 2019. Ship extraction using post CNN from high resolution optical remotely sensed images, Proc. of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), CN, Mar. 15-17, pp. 2531-2535.
|
12 |
Truong, T.N., T. Do Ngoc, B.N. Quang, and S. Le Tran, 2019. Combining Multi-Threshold Saliency with Transfer Learning for Ship Detection and Information Extraction From Optical Satellite Images. Proc. of 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Dalian, Liaoning, CN, Nov. 14-16, pp. 974-980.
|
13 |
Tang, J., C. Deng, G.B. Huang, and B. Zhao, 2014. Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine, IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1174-1185.
DOI
|
14 |
Wang, J., T. Zheng, P. Lei, and X. Bai, 2019. A hierarchical convolution neural network (CNN)-based ship target detection method in spaceborne SAR imagery, Remote Sensing, 11(6): 620.
DOI
|
15 |
Capella X-SAR (Synthetic Aperture Radar) Constellation, https://directory.eoportal.org/web/eoportal/satellite-missions/content/-/article/capella-x-sar, Accessed on Apr. 26, 2021.
|
16 |
Hwang, J.I. and H.S. Jung, 2018. Automatic ship detection using the artificial neural network and support vector machine from X-band SAR satellite images, Remote Sensing, 10(11): 1799.
DOI
|
17 |
Kang, K.M. and D.J. Kim, 2019. Ship velocity estimation from ship wakes detected using convolutional neural networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(11): 4379-4388.
DOI
|
18 |
Zhang, T. and X. Zhang, 2019. High-speed ship detection in SAR images based on a grid convolutional neural network, Remote Sensing, 11(10): 1206.
DOI
|
19 |
Ministry of Science and ICT, 2021. The 2nd Comprehensive Plan for Using Satellite Information Implementation plan in 2021, Sejong City, KR (in Korean with English abstract).
|
20 |
Korea Aerospace Research Institute (KARI), 2019. A Feasibility Study for the Construction of the Satellite Based National Disaster Response System mainly using Clustered Microsatellites, Korea Aerospace Research Institute, Daejeon, KR (in Korean with English abstract).
|
21 |
ICEYE. 2021. SAR product guide document Version 4.1, ICEYE, Espoo, FI.
|
22 |
Capella, 2020. Capella Space SAR Imagery Products Guide, Capella Space, San Francisco, CA, USA.
|
23 |
EOPortal Directory, 2021. Rapideye, https://directory.eoportal.org/web/eoportal/satellite-missions, Accessed on Jun. 11, 2021.
|
24 |
European Space Agency, https://earth.esa.int, Accessed on Jun. 22, 2021.
|
25 |
Bentes, C., D. Velotto, and B. Tings, 2017. Ship classification in TerraSAR-X images with convolutional neural networks, IEEE Journal of Oceanic Engineering, 43(1): 258-266.
DOI
|