Citation: | Jingwei Gu, Xiuzhong Li, Yijun He. A speckle noise suppression method based on surface waves investigation and monitoring data[J]. Acta Oceanologica Sinica, 2023, 42(1): 131-141. doi: 10.1007/s13131-022-2103-4 |
Achim A, Tsakalides P, Bezerianos A. 2003. SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling. IEEE Transactions on Geoscience and Remote Sensing, 41(8): 1773–1784. doi: 10.1109/tgrs.2003.813488
|
Ahmed S M, Eldin F A E, Tarek A M. 2010. Speckle noise reduction in SAR images using adaptive morphological filter. In: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications. Cairo, Egypt: IEEE, 260–265
|
Argenti F, Alparone L. 2002. Speckle removal from SAR images in the undecimated wavelet domain. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2363–2374. doi: 10.1109/tgrs.2002.805083
|
Bi Fan, Song Jinbao, Wu Kejian, et al. 2015. Evaluation of the simulation capability of the Wavewatch III model for Pacific Ocean wave. Acta Oceanologica Sinica, 34(9): 43–57. doi: 10.1007/s13131-015-0737-1
|
Caudal G, Hauser D, Valentin R, et al. 2014. KuROS: a new airborne ku-band doppler radar for observation of surfaces. Journal of Atmospheric and Oceanic Technology, 31(10): 2223–2245. doi: 10.1175/jtech-d-14-00013.1
|
Chen Sizhe, Wang Haipeng, Xu Feng, et al. 2016. Target classification using the deep convolutional networks for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4806–4817. doi: 10.1109/tgrs.2016.2551720
|
Chierchia G, Cozzolino D, Poggi G, et al. 2017. SAR image despeckling through convolutional neural networks. In: Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, USA: IEEE, 5438–5441
|
Deledalle C A, Denis L, Tupin F. 2009. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing, 18(12): 2661–2672. doi: 10.1109/tip.2009.2029593
|
Deledalle C A, Denis L, Tupin F, et al. 2015. NL-SAR: a unified nonlocal framework for resolution-preserving (pol)(in)SAR denoising. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 2021–2038. doi: 10.1109/tgrs.2014.2352555
|
Dong Xiaolong, Zhu Di, Lin Wenming, et al. 2011. Status and recent progresses of development of the scatterometer of CFOSAT. In: Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, Canada: IEEE, 961–964
|
Gallagher S, Gleeson E, Tiron R, et al. 2016. Wave climate projections for Ireland for the end of the 21st century including analysis of EC-Earth winds over the North Atlantic Ocean. International Journal of Climatology, 36(4): 4592–4607. doi: 10.1002/joc.4656
|
Gallagher S, Tiron R, Dias F. 2014. A long-term nearshore wave hindcast for Ireland: Atlantic and Irish Sea coasts (1979–2012). Ocean Dynamics, 64(8): 1163–1180. doi: 10.1007/s10236-014-0728-3
|
Guo Lanli, Perrie W, Long Zhenxia, et al. 2015. The impacts of climate change on the autumn North Atlantic wave climate. Atmosphere-Ocean, 53(5): 491–509. doi: 10.1080/07055900.2015.1103697
|
Hauser D, Caudal G, Rijckenberg G J, et al. 1992. RESSAC: a new airborne FM/CW radar ocean wave spectrometer. IEEE Transactions on Geoscience and Remote Sensing, 30(5): 981–995. doi: 10.1109/36.175333
|
Hauser D, Soussi E, Thouvenot E, et al. 2001. SWIMSAT: a real-aperture radar to measure directional spectra of ocean waves from space—main characteristics and performance simulation. Journal of Atmospheric and Oceanic Technology, 18(3): 421–437. doi: 10.1175/1520-0426(2001)018<0421:SARART>2.0.CO;2
|
Hauser D, Tison C, Amiot T, et al. 2017. SWIM: the first spaceborne wave scatterometer. IEEE Transactions on Geoscience and Remote Sensing, 55(5): 3000–3014. doi: 10.1109/tgrs.2017.2658672
|
Hauser D, Tison C, Lefèvre J M, et al. 2010. Measuring ocean waves from space: objectives and characteristics of the China-France oceanography SATellite (CFOSAT). In: Proceedings of the ASME 2010 29th International Conference on Ocean, Offshore and Arctic Engineering. Shanghai, China: ASME, 85–90
|
Hauser D, Tourain C, Hermozo L, et al. 2021. New observations from the SWIM radar on-board CFOSAT: instrument validation and ocean wave measurement assessment. IEEE Transactions on Geoscience and Remote Sensing, 59(1): 5–26. doi: 10.1109/tgrs.2020.2994372
|
He Hailun, Xu Yao. 2016. Wind-wave hindcast in the Yellow Sea and the Bohai Sea from the year 1988 to 2002. Acta Oceanologica Sinica, 35(3): 46–53. doi: 10.1007/s13131-015-0786-5
|
Jackson F C, Walton W T, Baker P L. 1985a. Aircraft and satellite measurement of ocean wave directional spectra using scanning-beam microwave radars. Journal of Geophysical Research, 90(C1): 987–1004. doi: 10.1029/jc090ic01p00987
|
Jackson F C, Walton W T, Peng C Y. 1985b. A comparison of in situ and airborne radar observations of ocean wave directionality. Journal of Geophysical Research, 90(C1): 1005–1018. doi: 10.1029/jc090ic01p01005
|
Kwak Y, Song W J, Kim S E. 2019. Speckle-noise-invariant convolutional neural network for SAR target recognition. IEEE Geoscience and Remote Sensing Letters, 16(4): 549–553. doi: 10.1109/lgrs.2018.2877599
|
Lee J S. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2(2): 165–168. doi: 10.1109/tpami.1980.4766994
|
Lee J S. 1981a. Refined filtering of image noise using local statistics. Computer Graphics and Image Processing, 15(4): 380–389. doi: 10.1016/s0146-664x(81)80018-4
|
Lee J S. 1981b. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing, 17(1): 24–32. doi: 10.1016/s0146-664x(81)80005-6
|
Lee J S. 1983. A simple speckle smoothing algorithm for synthetic aperture radar images. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(1): 85–89,
|
Mohan E, Rajesh A, Sunitha G, et al. 2021. A deep neural network learning-based speckle noise removal technique for enhancing the quality of synthetic-aperture radar images. Concurrency and Computation: Practice and Experience, 33(13): e6239. doi: 10.1002/cpe.6239
|
Morgan D A E. 2015. Deep convolutional neural networks for ATR from SAR imagery. In: Proceedings of SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII. Baltimore, USA: International Society for Optical Engineering, 94750F
|
Owirka G J, Verbout S M, Novak L M. 1999. Template-based SAR ATR performance using different image enhancement techniques. In: Proceedings of SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI. Orlando, USA: International Society for Optical Engineering
|
Patnaik R, Casasent D. 2005. MINACE filter classification algorithms for ATR using MSTAR data. In: Proceedings of SPIE 5807, Automatic Target Recognition XV. Orlando, USA: International Society for Optical Engineering, 100–111. doi: 10.1117/12.603065
|
Quach B, Glaser Y, Stopa J E, et al. 2021. Deep learning for predicting significant wave height from synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 1859–1867. doi: 10.1109/tgrs.2020.3003839
|
Raju K M S, Nasir M S, Devi T M. 2013. Filtering techniques to reduce speckle noise and image quality enhancement methods on satellite images. IOSR Journal of Computer Engineering, 15(4): 10–15. doi: 10.9790/0661-1541015
|
Shao Weizeng, Hu Yuyi, Yang Jingsong, et al. 2018. An empirical algorithm to retrieve significant wave height from Sentinel-1 synthetic aperture radar imagery collected under cyclonic conditions. Remote Sensing, 10(9): 1367. doi: 10.3390/rs10091367
|
Sheng Yexin, Shao Weizeng, Li Shuiqing, et al. 2019. Evaluation of typhoon waves simulated by Wavewatch-III model in shallow waters around Zhoushan Islands. Journal of Ocean University of China, 18(2): 365–375. doi: 10.1007/s11802-019-3829-2
|
Singh P, Pandey R S. 2016. Speckle noise: modelling and implementation. International Journal of Circuit Theory and Applications, 9(17): 8717–8727. doi: 10.1175/waf-d-16-0078.1
|
Tison C, Amiot T, Bourbier J, et al. 2009. Directional wave spectrum estimation by SWIM instrument on CFOSAT. In: Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town, South Africa: IEEE, V-312–V-315
|
Tison C, Hauser D, Castillan P. 2019. Swim Products Users Guide. Toulouse: Centre National d’Etudes Spatiales
|
Vandemark D, Jackson F C, Walsh E J, et al. 1994. Airborne radar measurements of ocean wave spectra and wind speed during the grand banks ERS-1 SAR wave experiment. Atmosphere-Ocean, 32(1): 143–178. doi: 10.1080/07055900.1994.9649493
|
Wang He, Mouche A, Husson R, et al. 2022. Assessment of ocean swell height observations from Sentinel-1A/B wave mode against buoy in situ and modeling hindcasts. Remote Sensing, 14(4): 862. doi: 10.3390/rs14040862
|
Wang He, Wang Jing, Yang Jingsong, et al. 2018. Empirical algorithm for significant wave height retrieval from wave mode data provided by the Chinese satellite Gaofen-3. Remote Sensing, 10(3): 363. doi: 10.3390/rs10030363
|
Yamazaki D, Ikeshima D, Tawatari R, et al. 2017. A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11): 5844–5853. doi: 10.1002/2017gl072874
|
Yu Yongjian, Acton S T. 2002. Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11): 1260–1270. doi: 10.1109/tip.2002.804276
|
Zheng Kaiwen, Osinowo A A, Sun Jian, et al. 2018. Long-term characterization of sea conditions in the East China Sea using significant wave height and wind speed. Journal of Ocean University of China, 17(4): 733–743. doi: 10.1007/s11802-018-3484-z
|
Zheng Kaiwen, Sun Jian, Guan Changlong, et al. 2016. Analysis of the global swell and wind sea energy distribution using WAVEWATCH III. Advances in Meteorology, 2016: 8419580. doi: 10.1155/2016/8419580
|