基于SMAP卫星雷达资料的海冰检测技术
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摘要
利用SMAP卫星雷达资料与美国国家冰雪数据中心(NSIDC)发布的近实时逐日极区网格化海冰密集度数据建立匹配数据集,分析了海冰和海水的L波段雷达后向散射特性差异,建立了基于线性判别分析算法的海冰检测算法。选择Sentinel-1A SAR极地地区的海冰影像对SMAP卫星雷达资料海冰检测产品进行实验验证,结果显示二者的海冰边缘线一致,说明SMAP海冰检测算法具有较高的精度。利用SMAP卫星雷达资料制作了北极和南极地区海冰覆盖图,计算了海冰覆盖面积,通过与美国国家冰雪数据中心(NSIDC)海冰覆盖面积比较发现,SMAP检测的北极地区海冰面积略大于NSIDC,相对偏差为3.3%,SMAP检测的南极地区海冰面积略小于NSIDC,相对偏差为1.8%,表明二者的覆盖面积基本一致,证实了SMAP海冰检测算法的精度。
This paper uses Soil Moisture Active Passive(SMAP) Satellite Radar data as well as the near-realtime daily polar meshing sea ice concentration data released by the National Snow and Ice Data Center(NSIDC)to establish the match-up dataset. A sea ice detection algorithm based on Fisher's linear discriminant analysis is developed by analyzing the difference of NRCS and polarization ratio between sea ice and open water. The extent and edge of sea ice from SMAP satellite radar data has been validated by Sentinel-1 A SAR images. The results show that SMAP sea ice edge is basically consistent with the Sentinel-1 A SAR image and that the sea ice detection algorithm has high accuracy. A sea ice chart of Arctic and Antarctic regions is produced with SMAP satellite radar data by using the sea ice detection algorithm, with the sea ice coverage calculated, which is then compared with the NSIDC data. It can be discovered that the area of Arctic sea ice detected by SMAP radar data is slightly larger than that from NSIDC data, while the Antarctic situation is just the opposite. The relative deviation in Arctic and Antarctic is 3.3% and 1.8%, respectively, which shows that their coverage area is roughly consistent. It at the same time verifies the precision of the SMAP sea ice detection algorithm.
This paper uses Soil Moisture Active Passive(SMAP) Satellite Radar data as well as the near-realtime daily polar meshing sea ice concentration data released by the National Snow and Ice Data Center(NSIDC)to establish the match-up dataset. A sea ice detection algorithm based on Fisher's linear discriminant analysis is developed by analyzing the difference of NRCS and polarization ratio between sea ice and open water. The extent and edge of sea ice from SMAP satellite radar data has been validated by Sentinel-1 A SAR images. The results show that SMAP sea ice edge is basically consistent with the Sentinel-1 A SAR image and that the sea ice detection algorithm has high accuracy. A sea ice chart of Arctic and Antarctic regions is produced with SMAP satellite radar data by using the sea ice detection algorithm, with the sea ice coverage calculated, which is then compared with the NSIDC data. It can be discovered that the area of Arctic sea ice detected by SMAP radar data is slightly larger than that from NSIDC data, while the Antarctic situation is just the opposite. The relative deviation in Arctic and Antarctic is 3.3% and 1.8%, respectively, which shows that their coverage area is roughly consistent. It at the same time verifies the precision of the SMAP sea ice detection algorithm.
引文
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[7]张晰.基于纹理特征分析的辽东湾SAR影像海冰检测[J].海洋科学进展,2008,26(3):386-393.
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[9]Synder J P.Map projection used by the U.S.Geological Survey[R].U.S.Geological Survey Bulletin 1532,1982.
[10]Zhou X,Chong J S,Yang X F,et al,Ocean surface wind retrieval using SMAP L-band SAR[J].IEEE J Sel Topics Appl Earth Observ Remote Sens,2017,10:65-74.
[11]Yang X Z,Clausi D A.Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs[J].IEEE J Sel Topics Appl Earth Observ Remote Sens,2012,5(5):1383-1393.
[12]Fisher R A.The use of multiple measures in taxonomic problems[J].Ann Eugenics,1936,7:179-188.
[13]王海珍.基于LDA的人脸识别技术研究[D].西安:西安电子科技大学,2010.
[2]Remund Q P,Long D G.Sea ice extent mapping using Ku band scatterometer data[J].J Geophys Res Ocean,1999,104:11515-11527.
[3]Remund Q P Long D G.A decade of Quik SCAT scatterometer sea ice extent data[J].IEEE Trans Geosci Remote Sens,2014,52(7):4281-4290.
[4]Anderson H.S,Long D G.Sea ice mapping method for Sea Winds[J].IEEE Trans Geosci Remote Sens.2005,43:647-657.
[5]Rivas M B,Stoffelen A.New Bayesian algorithm for sea ice detection with Quik SCAT[J].IEEE Trans Geosci Remote Sens,2011,49:1894-1901.
[6]Deng H W,David A C.Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel markov random field model[J].IEEE Trans Geosci Remote Sens,2005,43(3):528-538.
[7]张晰.基于纹理特征分析的辽东湾SAR影像海冰检测[J].海洋科学进展,2008,26(3):386-393.
[8]Li M M,Zhao C F,Zhao Y,et al.Polar sea ice monitoring using HY-2A scatterometer measurements[J].Remote Sens,2016,8:688.
[9]Synder J P.Map projection used by the U.S.Geological Survey[R].U.S.Geological Survey Bulletin 1532,1982.
[10]Zhou X,Chong J S,Yang X F,et al,Ocean surface wind retrieval using SMAP L-band SAR[J].IEEE J Sel Topics Appl Earth Observ Remote Sens,2017,10:65-74.
[11]Yang X Z,Clausi D A.Evaluating SAR sea ice image segmentation using edge-preserving region-based MRFs[J].IEEE J Sel Topics Appl Earth Observ Remote Sens,2012,5(5):1383-1393.
[12]Fisher R A.The use of multiple measures in taxonomic problems[J].Ann Eugenics,1936,7:179-188.
[13]王海珍.基于LDA的人脸识别技术研究[D].西安:西安电子科技大学,2010.