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Ocean internal wave detection in SAR images by combining superpixel segmentation and saliency features

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：cuiguangxi20@mails.ucas.ac.cn
  • Introduction：崔光曦，研究方向为海洋微波遥感。E-mail： cuiguangxi20@mails.ucas.ac.cn
CUI Guangxi13,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China

  • Email：duyanlei@aircas.ac.cn
  • Introduction：杜延磊，研究方向为海洋微波遥感。E-mail： duyanlei@aircas.ac.cn
DU Yanlei12*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China

YANG Xiaofeng12,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Macau, Macau 999078, China

WANG Sheng14,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    University of Chinese Academy of Sciences, Beijing 100049, China

XU Xuefeng13

resumen

Ocean internal waves are a commonly observed catastrophic mesoscale oceanic phenomenon, which attracts great attention due to its considerable threat to marine military and marine engineering. With the rapid development of science and technology, the ocean internal wave remote sensing detection method has attracted increasing attention. At present, remote sensing methods used for internal wave observation can be divided into Synthetic Aperture Radar (SAR), visible light, and infrared by frequency band. Among them, SAR has the advantages of all-day, all-weather, and high-resolution, which is especially well-suited for remote sensing investigation of oceanic internal waves with frequent cloud coverage areas. To achieve accurate detection of ocean internal waves using SAR images and to solve the problem that conventional detection algorithms are susceptible to SAR speckle noise interference, this study proposes a SAR ocean internal wave detection algorithm based on superpixel segmentation and global saliency features.First, the SAR image is segmented into feature-uniform superpixels using the Simple Linear Iterative Clustering (SLIC) algorithm. The SLIC algorithm combines neighboring pixels with similar features into superpixels. The superpixels not only enhance the continuity between the inner wave pixels but also suppress the speckle noise interference. Then, the gradient feature, gray scale feature, and spatial feature of the superpixel are used to construct the internal wave saliency feature vector and calculate its global saliency. On the basis of the saliency, the threshold segmentation algorithm is used to extract the internal wave superpixels. Experiments are conducted on GF-3 and ERS-1 images, which show that the constructed internal wave saliency feature vector is beneficial to detect more internal wave stripes. Finally, the label image indicating the internal wave regions is generated in accordance with the spectral characteristics of internal wave and used to correct the internal wave detection result in previous step.We conducted a detection experiment of internal wave bright stripes on five SAR images with a resolution of approximately 10 m. The experimental results show that the proposed method has good detection accuracy for these five high-resolution SAR internal wave images. The average F1 score of the internal wave detection for the five scene experimental data of our method could reach 0.884, and the average false alarm rate is 0.009.By comparing the internal wave detection results and related evaluation indexes of our method with the classical canny operator and the deep learning U-Net method, the effectiveness and robustness of our proposed method in high-resolution SAR ocean internal wave detection are demonstrated, which is cr to improve the inversion accuracy of internal wave wavelength and amplitude.

palabra clave

remote sensing;Ocean internal wave;superpixel segmentation;salient feature detection;fourier energy spectrum;synthetic aperture radar

References

  1. 1.
    Alpers W. 1985. Theory of radar imaging of internal waves. Nature, 314(6008): 245-247
  2. 2.
    Apel J R. 2003. A new analytical model for internal solitons in the ocean. Journal of Physical Oceanography, 33(11): 2247-2269
  3. 3.
    Apel J R, Holbrook J R, Liu A K and Tsai J J. 1985. The sulu sea internal soliton experiment. Journal of Physical Oceanography, 15(12): 1625-1651
  4. 4.
    Bao S D, Meng J M, Sun L N and Liu Y X. 2020. Detection of ocean internal waves based on Faster R-CNN in SAR images. Journal of Oceanology and Limnology, 38(1): 55-63
  5. 5.
    Borji A, Cheng M M, Hou Q B, Jiang H Z and Li J. 2019. Salient object detection: a survey. Computational Visual Media, 5(2): 117-150
  6. 6.
    Cai S Q, Xie J S and He J L. 2012. An overview of internal solitary waves in the South China Sea. Surveys in Geophysics, 33(5): 927-943
  7. 7.
    Celona S, Merrifield S T, De Paolo T, Kaslan N, Cook T, Terrill E J and Colosi J A. 2021. Automated detection, classification, and tracking of internal wave signatures using X-band radar in the inner shelf. Journal of Atmospheric and Oceanic Technology, 38(4): 789-803
  8. 8.
    Chen J, Chen B, Tao R H and Xu S Q. 2014. An automatic extraction method of SAR images of ocean internal wave parameters. Journal of Ocean Technology, 33(6): 20-27
  9. 9.
    Chen J, Chen B and Xu S Q. 2010. Detection of ocean features in SAR images with 2D continuous wavelet power spectrum. Acta Electronica Sinica, 38(9): 2128-2133
  10. 10.
    Ding C, Zhang B T, Gao G X, Chen B and Wu B Q. 2012. Feature detection of ocean internal wave in SAR image. Hydrographic Surveying and Charting, 32(5): 15-18
  11. 11.
    Du Y L, Gao F, Liu T and Yang J. 2021. Statistical modeling and characteristic analysis of polarimetric SAR sea clutter at X-band based on numerical simulations. Systems Engineering and Electronics, 43(10): 2742-2755
  12. 12.
    Du Y L, Yang X F, Wang S, Yin J J, Yang H Z and Yang J. 2023. Numerical investigation on the spatial ergodicity of ocean radar scattering and sea clutter amplitude statistical characteristics. Systems Engineering and Electronics, 45(12): 3806-3818
  13. 13.
    Fan K G, Huang W G, Chen P, Yu X X, Fu B, Chang J F and Pang T B. 2011. Automatic detection of ocean internal wave from SAR image//Proceedings of SPIE 8175, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2011. Prague: SPIE: 817513 [DOI: 10.1117/12.890873]
  14. 14.
    Fang X H, Wu W and Qiu D Z. 1999. On internal waves and finestructure in the regions surrounding the Nansha Islands. Journal of Ocean University of Qingdao, (4): 537-542
  15. 15.
    Guo W, Wang Y B and Tang Q X. 2018. Marine internal wave feature extractin of SAR image based on wavelet transform. China Science and Technology Information, (24): 80-81
  16. 16.
    He Z M, Ding H Y and An B Q. 2022. E-Unet: a atrous convolution-based neural network for building extraction from high-resolution remote sensing images. Acta Geodaetica et Cartographica Sinica, 51(3): 457-467
  17. 17.
    Hogan G G, Marsden J B and Henry J C. 1991. On the detection of internal waves in high resolution SAR imagery using the Hough transform//OCEANS 91 Proceedings. Honololu, USA: IEEE: 1363-1369
  18. 18.
    Huang X D, Chen Z H, Zhao W, Zhang Z W, Zhou C, Yang Q X and Tian J W. 2016. An extreme internal solitary wave event observed in the northern South China Sea. Scientific Reports, 6(1): 30041
  19. 19.
    Jiang Y, Zhang M, Bai X Y and Hua S H. 2022. Early warning and monitoring technology of marine internal wave based on acoustic vector field processing. Electronic Science and Technology, 35(3): 25-31
  20. 20.
    Li J J, Cao J N, Zhu Y Y and Cheng B B. 2020. Built-up area detection from high resolution remote sensing images using geometric features. Journal of Remote Sensing (in Chinese), 24(3): 233-244
  21. 21.
    Li X F, Liu B, Zheng G, Ren Y B, Zhang S S, Liu Y J, Gao L, Liu Y H, Zhang B and Wang F. 2020. Deep-learning-based information mining from ocean remote-sensing imagery. National Science Review, 7(10): 1584-1605
  22. 22.
    Qin J X, Katsnelson B, Li Z L, Zhang R H and Luo W Y. 2016. Intensity fluctuations due to the motion of internal solitons in shallow water. Acta Acustica, 41(2): 145-153
  23. 23.
    Ren X F and Malik J. 2003. Learning a classification model for segmentation//Proceedings Ninth IEEE International Conference on Computer Vision. Nice, France: IEEE: 10-17
  24. 24.
    Rodenas J A and Garello R. 1998. Internal wave detection and location in SAR images using wavelet transform. IEEE Transactions on Geoscience and Remote Sensing, 36(5): 1494-1507
  25. 25.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241
  26. 26.
    Sun H L, Wang Y R, Jia T, Shi Y N and Li X M. 2023. Faster R-CNN based oceanic internal wave detection from SAR images in the South China Sea. National Remote Sensing Bulletin, 27(4): 905-918
  27. 27.
    Wang S K, Dong Q H, Duan L H, Sun Y J, Jian M W, Li J Z and Dong J Y. 2019. A fast internal wave detection method based on PCANet for ocean monitoring. Journal of Intelligent Systems, 28(1): 103-113
  28. 28.
    Wang Y, Yang Y, Wang B S, Wang T, Bu X H and Wang C Y. 2019. Building segmentation in high-resolution remote sensing image through deep neural network and conditional random fields. Journal of Remote Sensing (in Chinese), 23(6): 1194-1208
  29. 29.
    Xu S Q, Chen J, Li T T and Tao R H. 2019. Automatic feature detection of ocean internal wave SAR image based on hierarchical clustering. Computer Science and Application, 9(6): 1118-1125
  30. 30.
    Xuan D D and Wang J. 2018. A survey of bottom-up image saliency detection. Journal of Yichun University, 40(9): 50-55
  31. 31.
    Yin J J, Wang T, Du Y L, Liu X Y, Zhou L J and Yang J. 2022. SLIC superpixel segmentation for polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 60: 5201317
  32. 32.
    Zhang T, Yang X G, Lu R T, Xie X L and Liu C. 2022. Key-point based method for ship detection in remote sensing images. Systems Engineering and Electronics, 44(8): 2437-2447
  33. 33.
    Zhang X D, Wang H Y, Wang S, Liu Y L, Yu W D, Wang J, Xu Q and Li X F. 2022. Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model. Remote Sensing of Environment, 272: 112940
  34. 34.
    Zhao Z X, Liu B Q and Li X F. 2014. Internal solitary waves in the China seas observed using satellite remote-sensing techniques: a review and perspectives. International Journal of Remote Sensing, 35(11/12): 3926-3946
  35. 35.
    Zheng Y G, Zhang H S, Li X L and Zhang Z H. 2020. Detection algorithm of internal waves in the South China Sea based on boundary characteristics of remote sensing image. Journal of Tropical Oceanography, 39(6): 41-56
  36. 36.
    Zheng Y G, Zhang H S and Wang Y Q. 2021. Stripe detection and recognition of oceanic internal waves from synthetic aperture radar based on support vector machine and feature fusion. International Journal of Remote Sensing, 42(17): 6706-6724

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