참고문헌
- Brekke, C. and A.H. Solberg, 2005. Oil spill detection by satellite remote sensing, Remote sensing of environment, 95(1): 1-13. https://doi.org/10.1016/j.rse.2004.11.015
- Buades, A. and B. Coll, 2005. A non-local algorithm for image denoising, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2(8): 60-65.
- Chang, L., Z.S. Tang, S.H. Chang, and Y.L. Chang, 2008. A region-based GLRT detection of oil spills in SAR images, Pattern Recognition Letters, 29(14): 1915-1923. https://doi.org/10.1016/j.patrec.2008.05.022
- Fan, K., Y. Zhang, and H. Lin, 2010. Satellite SAR analysis and interpretation of oil spill in the offshore water of Hong Kong, Annals of GIS, 16(4): 269-275. https://doi.org/10.1080/19475683.2010.540259
- Fingas, M. and C. Brown, 2014. Review of oil spill remote sensing, Marine pollution bulletin, 83(1): 9-23. https://doi.org/10.1016/j.marpolbul.2014.03.059
- Kittler, J., and J. Illingworth, 1986. Minimum error thresholding, Pattern recognition, 19(1): 41-47. https://doi.org/10.1016/0031-3203(86)90030-0
- Kubat, M., R. C. Holte, and S. Matwin, 1998. Machine learning for the detection of oil spills in satellite radar images, Machine learning, 30(2): 195-215. https://doi.org/10.1023/A:1007452223027
- Kim, J.-C., D.-H. Kim, S.-H. Park, H.-S. Jung and H.-S. Shin, 2014. Application of Landsat images to Snow Cover Changes by Volcanic Activities at Mt. Villarica and Mt. Lliama, Chile, Korean Journal of Remote Sensing, 30(3): 341-350. https://doi.org/10.7780/kjrs.2014.30.3.1
- Kim, D., H.-S. Jung, and W.K. Baek, 2016. Comparative Analysis among Radar Image Filters for Flood Mapping, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 34(1): 43-52. (in Korean with English abstract). https://doi.org/10.7848/ksgpc.2016.34.1.43
- Lee, S., and H.-S. Jung, 2016. Multi-temporal Analysis of Deforestation in Pyeongyang and Hyesan, North Korea, Korean Journal of Remote Sensing, 32(1): 1-11. https://doi.org/10.7780/kjrs.2016.32.1.1
- Moreira, A., 1991. Improved multilook techniques applied to SAR and SCANSAR imagery. IEEE Transactions on geoscience and remote sensing, 29(4): 529-534. https://doi.org/10.1109/36.135814
- Marghany, M., 2016. Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm, Acta Geophysica, 64(5): 1916-1941. https://doi.org/10.1515/acgeo-2016-0047
- Nirchio, F., M. Sorgente, A. Giancaspro, W. Biamino, E. Parisato, R. Ravera, and P. Trivero, 2005. Automatic detection of oil spills from SAR images, International Journal of Remote Sensing, 26(6): 1157-1174. https://doi.org/10.1080/01431160512331326558
- Ozkan, C., C. Ozturk, F. Sunar, and D. abd Karaboga, 2011. The artificial bee colony algorithm in training artificial neural network for oil spill detection, Neural Network World, 21(6): 473-492. https://doi.org/10.14311/NNW.2011.21.028
- Park, S.-H., M.-J. Lee, and H.-S. Jung, 2012. Analysis on the Snow Cover Variations at Mt. Kilimanjaro Using Landsat Satellite Images, Korean Journal of Remote Sensing, 28(4): 409-420. (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.4.5
- Shu, Y., J. Li, H. Yousif, and G. Gomes, 2010. Darkspot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring, Remote Sensing of Environment, 114(9): 2026-2035. https://doi.org/10.1016/j.rse.2010.04.009
- Topouzelis, K., V. Karathanassi, P. Pavlakis, and D. Rokos, 2007. Detection and discrimination between oil spills and look-alike phenomena through neural networks, ISPRS Journal of Photogrammetry and Remote Sensing, 62(4):264-270. https://doi.org/10.1016/j.isprsjprs.2007.05.003
- Xu, L., M. Javad Shafiee, A. Wong, F. Li, L. Wang, and D. Clausi, 2015. Oil spill candidate detection from SAR imagery using a thresholding-guided stochastic fully-connected conditional random field model, Proc. of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Hynes Convention Center, Boston, MA, USA, 79-86.
- Zhang, Y., H. Lin, Q. Liu, J. Hu, X. Li, and K. Yeung, 2012. Oil-spill monitoring in the coastal waters of Hong Kong and Vicinity, Marine Geodesy, 35(1): 93-106. https://doi.org/10.1080/01490419.2011.637872
피인용 문헌
- Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors vol.20, pp.6, 2017, https://doi.org/10.2166/hydro.2018.120
- 다중시기 위성 레이더 영상을 활용한 변화탐지 기술 리뷰 vol.35, pp.5, 2017, https://doi.org/10.7780/kjrs.2019.35.5.1.10
- Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network vol.12, pp.2, 2017, https://doi.org/10.3390/rs12020253
- Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model vol.10, pp.21, 2020, https://doi.org/10.3390/app10217751
- Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network vol.13, pp.16, 2017, https://doi.org/10.3390/rs13163203
- Hyperspectral linear unmixing based on collaborative sparsity and multi-band non-local total variation vol.43, pp.1, 2017, https://doi.org/10.1080/01431161.2021.1996653