Volume 42 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
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
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

A speckle noise suppression method based on surface waves investigation and monitoring data

doi: 10.1007/s13131-022-2103-4
More Information
  • Corresponding author: Email: yjhe@nuist.edu.cn
  • Received Date: 2022-07-05
  • Accepted Date: 2022-09-13
  • Available Online: 2023-01-17
  • Publish Date: 2023-01-25
  • The internal energy distribution of waves can be described using ocean-wave spectra. In many ways, obtaining wave spectra on a global scale is critical. Surface waves investigation and monitoring onboard the Chinese-French oceanography satellite is the first space-borne instrument for detecting wave spectra specially, which was launched on October 29, 2018. It can avoid the shortage of synthetic aperture radar detection results while still having some problems, especially with the effects of speckle noise. In this study, a method to suppress the speckle noise is proposed. First, the empirical formula for background speckle noise is established. Second, many spatio-temporal representative fluctuation spectra are classified and averaged. Third, rational transfer function filtering is used to obtain speckle noise close to the along-track direction. Finally, a signal-to-noise ratio threshold is used to suppress the abnormal speckle noise. This method solves the problems existing in previous denoising methods, such as excessive denoising in the along-track direction and the inability of some abnormal noises to be denoised in the two-dimensional directional wave spectra.
  • loading
  • 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
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)  / Tables(3)

    Article Metrics

    Article views (439) PDF downloads(22) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return