联合超像素分割和显著性特征的SAR海洋内波检测
Ocean Internal Wave Detection in SAR Images by Combining Superpixel Segmentation and Saliency Features
- 2023年 页码：1-15
网络出版日期： 2023-07-03
DOI： 10.11834/jrs.20232433
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摘要
海洋内波是一种常见的致灾性中尺度海洋现象，因其对海洋军事和海洋工程等存在巨大威胁而被广泛关注。为了实现合成孔径雷达（SAR）图像海洋内波的准确检测，解决传统检测算法易受斑噪干扰的问题，本文提出了一种基于超像素分割和全局显著性特征的SAR海洋内波检测算法。首先，基于简单线性迭代聚类算法（SLIC）将SAR图像分割成特征均一的超像素；然后，利用超像素的梯度特征、灰度特征及空间特征构建内波显著性特征向量，计算其全局显著性并基于显著度提取内波超像素；最后，根据内波在傅立叶能量谱上的特征对内波区域和非内波区域进行标记并生成标签图像，用于对显著性检测结果进行校正。实验结果表明：本文方法对5景实验数据的内波条纹检测平均F1分数可达到0.884、平均虚警率为0.009，证明了本文方法在不降低SAR图像空间分辨率的条件下可以有效抑制斑噪的影响，实现高分辨率SAR海洋内波条纹的准确检测。
Abstract
Objective Ocean internal waves are a commonly observed catastrophic mesoscale oceanic phenomenon
which is of great attention due to its significant threat to the marine military and marine engineering. With the rapid development of science and technology
the ocean internal wave remote sensing detection method has attracted more and more attention. At present
remote sensing methods used for internal wave observation can be divided into synthetic aperture radar (SAR)
visible light
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. In order to achieve accurate detection of ocean internal waves using synthetic aperture radar (SAR) images and to solve the problem that conventional detection algorithms are susceptible to SAR speckle noise interference
this paper proposes a SAR ocean internal wave detection algorithm based on superpixel segmentation and global saliency features.Method Firstly
the SAR image is segmented into feature-uniform superpixels using the simple linear iterative clustering algorithm (SLIC). 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 super-pixel are used to construct the internal wave saliency feature vector and calculate its global saliency. Based on the saliency
threshold segmentation algorithm is used to extract the internal wave superpixels. Experiments are conducted on GF-3 image and ERS-1 image
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 according to the spectral characteristics of internal wave and used to correct the internal wave detection result in previous step.Result We carried out the detection experiment of internal wave bright stripes on five SAR images with a resolution of about 10 meters. 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.Conclusion 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 of great significance to improve the inversion accuracy of internal wave wavelength and amplitude.
关键词
海洋内波超像素分割显著性特征检测傅立叶能量谱合成孔径雷达
Keywords
Ocean internal wavesuperpixel segmentationsalient feature detectionFourier energy spectrumsynthetic aperture radar
references
Alpers. 1985. Theory of radar imaging of internal waves. Nature, 314(6008): 245–247. [DOI:10.1016/0198-0254(85)92668-8http://dx.doi.org/10.1016/0198-0254(85)92668-8]
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. [DOI:10.1175/15200485(1985)015<1625: Tssise>2.0.Co;2http://dx.doi.org/10.1175/15200485(1985)015<1625:Tssise>2.0.Co;2]
Apel J R. 2003. A new analytical model for internal solitons in the ocean. Journal of Physical Oceanography, 2003, 33(11):55-122. [DOI:10.1175/1520-0485(2003)033<2247:ANAMFI>2.0.CO;2http://dx.doi.org/10.1175/1520-0485(2003)033<2247:ANAMFI>2.0.CO;2]
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, 38:55-63. [DOI:10. 1007/s00343-019-9028-6http://dx.doi.org/10.1007/s00343-019-9028-6]
Borji A, Cheng M M, Hou Q B, Jiang H Z, and Li J. 2019. Salient object detection: A survey. Computational Visual Media, 5:117-50. [DOI:10.1007/s41095-019-0149-9http://dx.doi.org/10.1007/s41095-019-0149-9]
Cai S, Xie J, and He J. 2012. An Overview of Internal Solitary Waves in the South China Sea. Review Surveys in Geophysics, 33: 927–943. [DOI:10.1007/s10712-012-9176-0http://dx.doi.org/10.1007/s10712-012-9176-0.]
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: 789-803. [DOI:10.1175/jtech-d-20-0129.1http://dx.doi.org/10.1175/jtech-d-20-0129.1]
Chen J, Chen B, and Xu S Q. 2010. Detection of Ocean Features in SAR Images with 2D Continuous Wavelet Power Spectrum. Journal of Electronics, 38(21):28-33.
陈捷, 陈标, 许素芹. 2010. 基于二维连续小波变换的SAR图像海洋现象特征检测. 电子学报 38(21):28-33
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: 20-27.
陈捷, 陈标, 陶荣华, 许素芹. 2014. SAR图像海洋内波参数自动提取方法. 海洋技术学报, 33: 20-27.
Ding C, Zhang B T, Gao X G, Chen B, and Wu B Q. 2012. Feature Detection of Ocean Internal Wave in SAR Image. Hydrographic Surveying and Charting, 32(5):15-8.
丁灿, 张本涛, 高国兴, 陈标, 吴宝勤. 2012. 合成孔径雷达图像中海洋内波的特征检测.海洋测绘, 32(5):15-18. [DOI:10.3969/j.issn.16713044. 2012.05.005http://dx.doi.org/10.3969/j.issn.16713044.2012.05.005]
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, 2021, 43(10): 2742-2755.
杜延磊, 高帆, 刘涛, 杨健. 2021. 基于数值仿真的X波段极化SAR 海杂波统计建模与特性分析. 系统工程与电子技术, 43(10): 2742-2755.
DO1:10.12305/j.issn.1001-506X.2021.10.06
Du Y L, Yang X F, Wang S, Yin J J, Yang H Z, and Yang J. 2022. Numerical Investigation on the Spatial Ergodicity of Ocean Radar Scattering and Sea Clutter Amplitude Statistical Characteristics. Systems Engineering and Electronics.
杜延磊, 杨晓峰, 汪胜, 殷君君, 杨会章, 杨健. 2022. 海面雷达散射及其杂波幅度统计特性的空间遍历性数值仿真研究. 系统工程与电子技术.
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 the International Society for Optical Engineering, 8175:817513. [DOI: 10.1117/12.890873http://dx.doi.org/10.1117/12.890873]
Fang X H, Wu W, and Qiu D Z. 1999. On Internal Waves and Fine Structure in the Regions Surrounding the Nansha Islands. Journal of Ocean University of Qingdao, 1999(04):537-542.
方欣华,吴巍,仇德忠. 1999. 关于南沙海域内波与细结构研究. 青岛海洋大学学报(自然科学版), 1999(04): 537-542. [DOI:10.16441/j. cnki.hdxb.1999.04.001http://dx.doi.org/10.16441/j.cnki.hdxb.1999.04.001.]
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.
郭薇, 王亚波, 汤琦璇. 2018. 基于小波变换的SAR图像海洋内波特征提取.中国科技信息 24:80-81. [DOI:10.3969/j.issn.10018972. 2018.24.030http://dx.doi.org/10.3969/j.issn.10018972.2018.24.030]
He Z M, Ding H Y, and An B Q. 2022. E-U-Net:a atrous convolution-based neural network for building extraction from high-resolution remote sensing images. Acta Geodaetica et Cartographica Sinica, 51(3): 457-467.
何直蒙, 丁海勇,安炳琪. 2022. 高分辨率遥感影像建筑物提取的空洞卷积E-U-Net算法. 测绘学报, 51(3): 457-467. [DOI:10.11947/j.AGCS. 2022.20200601http://dx.doi.org/10.11947/j.AGCS.2022.20200601.]
Hogan G G, Marsden J B, and Henry J. 1991. On the detection of internal waves in high-resolution SAR imagery using the hough transform. Ocean Technologies and Opportunities in the Pacific for the 90s, Honolulu, Hi, USA Oct 01-03. [DOI:10. 1109/oceans.1991.606491http://dx.doi.org/10.1109/oceans.1991.606491]
Huang X D, Cen 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. Review Scientific Reports 6(1):10. [DOI:10.1038/srep30041].
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 Sci. &Tech, 35(3): 25-31.
姜煜, 张敏, 白兴宇, 华生辉. 2022. 基于声矢量场处理的海洋内波预警监测技术. 电子科技, 35(3): 25-31. [DOI:10.16180/j.cnki.issn1007-7820.2022.03.004http://dx.doi.org/10.16180/j.cnki.issn1007-7820.2022.03.004]
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（Chinese）, 24(3): 233-244.
李军军, 曹建农, 朱莹莹,程贝贝. 2020. 高分辨率遥感影像建筑区域局部几何特征提取. 遥感学报, 24(3): 233-244. [DOI:10.11834/jrs.20208506http://dx.doi.org/10.11834/jrs.20208506.]
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 7:1584-605. [DOI:10. 1093/nsr/nwaa047http://dx.doi.org/10.1093/nsr/nwaa047]
Qin J X, KATSNELSON Boris, 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, 41:145-53.
秦继兴, KATSNELSON Boris, 李整林, 张仁和, 骆文于. 2016. 浅海中孤立子内波引起的声能量起伏. 声学学报, 41: 145-53.Ren X F， and Malik J. 2003. Learning a classification model for segmentation， Proceedings Ninth IEEE International Conference on Computer Vision， 1：13-16. ［DOI：10.1109/ICCV. 2003.1238308http://dx.doi.org/10.1109/ICCV.2003.1238308.］
Rodenas J A， and Garello R. 1998. Internal wave detection and location in SAR images using wavelet transform. IEEE Trans. Geosci &Remote Sensing， 36：494-507. ［DOI：10.1109/36. 718853http://dx.doi.org/10.1109/36.718853］
Ronneberger Olaf， Philipp Fischer， and Thomas Brox. 2015. U-Net： Convolutional Networks for Biomedical Image Segmentation， CoRR， abs/1505.04597.［ DOI：10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28］
Sun H L， Wang Y R， Jia T， Si Y N， and Li X M. 2022. Faster R-CNN Based Oceanic Internal Waves Detection from SAR Images in the South China Sea. Journal of Remote Sensing.
孙宏亮， 王怡然， 贾童， 施英妮，李晓明. 2022. 基于Faster R-CNN网络的卫星SAR图像南海海洋内波自动检测. 遥感学报. ［DOI：10.11834/jrs.20211324http://dx.doi.org/10.11834/jrs.20211324.］
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， 23（6）： 1194–1208.
王宇， 杨艺， 王宝山， 王田， 卜旭辉，王传云. 2019. 深度神经网络条件随机场高分辨率遥感图像建筑物分割. 遥感学报， 23： 1194-1208. ［DOI：10.11834/jrs.20198141http://dx.doi.org/10.11834/jrs.20198141］
Wang S K， Qing H D， Liang H D， Yu J Y， Mu W J， Jian Z L， and Jun Y D. 2019. A Fast Internal Wave Detection Method Based on PCANet for Ocean Monitoring， Journal of Intelligent Systems. 28. ［DOI：10.1515/jisys-2017-0033http://dx.doi.org/10.1515/jisys-2017-0033］
Xuan D D， and Wang J.2018. A Survey of Bottom-up Image Saliency Detection. Journal of Yichun University， 40（9）：50-55.
宣东东， 汪军. 2018. 自底向上的图像显著性检测研究综述. 宜春学院学报， 40（9）：50-55.
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.
许素芹， 陈捷， 李婷婷， 陶荣华. 2019. 结合层次聚类的海洋内波SAR图像特征自动检测. 计算机科学与应用， 9（6）：1118-1125. ［DOI：10.12677/csa.2019.96126http://dx.doi.org/10.12677/csa.2019.96126］
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， 60：17. ［DOI：10.1109/tgrs.2020.3047126］.
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： 3926-3946. ［DOI：10.1080/01431161.2014. 916442http://dx.doi.org/10.1080/01431161.2014.916442］
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.
张涛， 杨小冈， 卢瑞涛， 谢学立， 刘闯. 2022. 基于关键点的遥感图像舰船目标检测. 系统工程与电子技术， 44（8）：2437-2447.［DOI：10.12305/j.issn.1001-506X.2022.08.07http://dx.doi.org/10.12305/j.issn.1001-506X.2022.08.07］
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， 272：15. ［DOI：10.1016/j.rse.2022.112940］.
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.
郑应刚， 张洪生， 李晓恋， 张周昊. 2020. 基于遥感影像边界特性对南海海洋内波检测算法研究. 热带海洋学报， 39（6）：41-56. ［DOI：10.11978/2019120http://dx.doi.org/10.11978/2019120］
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： 6710-6728. ［DOI：10.1080/01431161.2021.1943040http://dx.doi.org/10.1080/01431161.2021.1943040］
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中国地质大学(武汉) 地球物理与空间信息学院
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