结合不同极化数据的海冰漂移检测
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摘要
基于Sentinel-1遥感数据,对合成孔径雷达(SAR)极化特征数据在海冰漂移检测中的应用进行了深入研究。研究了水平发射水平接收(HH)和水平发射垂直接收(HV)极化数据在海冰漂移检测中的差异,分析了两种极化数据的优劣,并发现由两种极化数据检测到的特征信息具有互补的特性。通过特征融合的方法,将由两种极化数据得到的特征信息有效地应用于海冰漂移检测中。实验结果表明,利用所提出的方法得到的海冰漂移矢量在空间分布和覆盖率方面均得到了提高。该方法能够有效地应用于海冰漂移检测。
The remote sensing data obtained from the Sentinel-1 spacecraft allows the application of the synthetic aperture radar(SAR)polarization feature data to the detection of the sea-ice drift.The difference in the usage of the horizontal-horizontal(HH)and horizontal-vertical(HV)polarization data for the detection of the sea-ice drift is discussed in terms of their advantages and disadvantages.The information detected using these two kinds of polarization data is observed to be complementary.Using feature fusion,the feature information detected using these two kinds of polarization data can be used for monitoring the sea-ice drift.The experimental results indicate that the sea-ice drift vector obtained using the proposed method is improved in terms of the spatial distribution and coverage rate,suggesting that the proposed method can be effectively applied for monitoring the sea-ice drift.
The remote sensing data obtained from the Sentinel-1 spacecraft allows the application of the synthetic aperture radar(SAR)polarization feature data to the detection of the sea-ice drift.The difference in the usage of the horizontal-horizontal(HH)and horizontal-vertical(HV)polarization data for the detection of the sea-ice drift is discussed in terms of their advantages and disadvantages.The information detected using these two kinds of polarization data is observed to be complementary.Using feature fusion,the feature information detected using these two kinds of polarization data can be used for monitoring the sea-ice drift.The experimental results indicate that the sea-ice drift vector obtained using the proposed method is improved in terms of the spatial distribution and coverage rate,suggesting that the proposed method can be effectively applied for monitoring the sea-ice drift.
引文
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[16] Wang W B,Zhang X D,Fei P S,et al.The coherent speckle suppression method in SAR image based on Contourlet domain adaptive BivaShrink model[J].Acta Optica Sinica,2009,29(10):2726-2731.王文波,张晓东,费浦生,等.基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法[J].光学学报,2009,29(10):2726-2731.
[2] Komarov A,Barber D.Detection of sea ice motion from co-and cross-polarization RADARSAT-2images[C]∥2012IEEE International Geoscience and Remote Sensing Symposium,July 22-27,2012,Munich,Germany,2012:3277-3280.
[3] Muckenhuber S,Korosov A A,Sandven S.Opensource feature-tracking algorithm for sea ice drift retrieval from Sentinel-1 SAR imagery[J].The Cryosphere,2016,10(2):913-925.
[4] Korosov A A,Rampal P.A combination of feature trackingandpatternmatchingwithoptimal parametrization for sea ice drift retrieval from SAR data[J].Remote Sensing,2017,9(3):258.
[5] Men P.Study on sea ice drift tracking based on SAR images of large difference in resolution[D].Dalian:Dalian Maritime University,2017.门鹏.面向大分辨率差异SAR图像的海冰漂移跟踪研究[D].大连:大连海事大学,2017.
[6] Xiao H Y, Tian C Z, Yue C R. Analysis on backscatteringcharacteristicsofthepinus yunnanensis based on full-polarization SAR data[J].Journal of Fujian Forestry Science and Technology,2015,42(3):85-87,93.肖虹雁,田传召,岳彩荣.基于全极化SAR数据的云南松后向散射特征分析[J].福建林业科技,2015,42(3):85-87,93.
[7] Liu H Y.Study on parameter retrieval and type recognition of sea ice using wide-swath and multipolarization SAR data[D].Beijing:University of Chinese Academy of Sciences(Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences),2017.刘惠颖.宽幅多极化SAR海冰信息提取方法与类型识别研究[D].北京:中国科学院大学(中国科学院遥感与数字地球研究所),2017.
[8] Komarov A S,Barber D G.Sea ice motion tracking from sequential dual-polarization RADARSAT-2images[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(1):121-136.
[9] Li C S, Wang W J, Wang P B,et al.Current situation and development trends of spaceborne SAR technology[J].Journal of Electronics&Information Technology,2016,38(1):229-240.李春升,王伟杰,王鹏波,等.星载SAR技术的现状与发展趋势[J].电子与信息学报,2016,38(1):229-240.
[10] Hollands T, Dierking W. Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images:initial results[J].Annals of Glaciolog y,2011,52(57):311-317.
[11] Wu Q.Sea ice drift tracking in the Bohai Sea based on GOCI[D]. Hefei:Hefei University of Technology,2014.吴青.基于GOCI卫星的渤海海冰漂移监测[D].合肥:合肥工业大学,2014.
[12] Lowe D G. Object recognition from local scaleinvariant features[C]∥Proceedings of the Seventh IEEE International Conference on Computer Vision,September 20-27,1999,Kerkyra,Greece,1999:1150-1157.
[13] Lowe D G.Distinctiveimage features from scaleinvariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[14] Zhang M,LüX Q,Wang J K,et al.Research on sea ice drift detection technology based on Sentinel-1data in Beaufort Sea area[J].Marine Environmental Science,2018,37(2):287-293.张明,吕晓琪,王军凯,等.基于Sentinel-1数据波弗特海域海冰漂移检测技术研究[J].海洋环境科学,2018,37(2):287-293.
[15] Lu Z L,Jia X,Zhu W G,et al.Study on SAR image despeckling algorithm[J]. Journal of Sichuan Ordnance,2017,38(6):104-108.鲁自立,贾鑫,朱卫纲,等.SAR图像相干斑抑制方法综述[J].兵器装备工程学报,2017,38(6):104-108.
[16] Wang W B,Zhang X D,Fei P S,et al.The coherent speckle suppression method in SAR image based on Contourlet domain adaptive BivaShrink model[J].Acta Optica Sinica,2009,29(10):2726-2731.王文波,张晓东,费浦生,等.基于Contourlet变换的自适应SAR图像相干斑噪声抑制算法[J].光学学报,2009,29(10):2726-2731.