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相干区域长轴的快速估计方法及其应用

白璐 曹芳 洪文

白璐, 曹芳, 洪文. 相干区域长轴的快速估计方法及其应用[J]. 电子与信息学报, 2010, 32(3): 548-553. doi: 10.3724/SP.J.1146.2009.00211
引用本文: 白璐, 曹芳, 洪文. 相干区域长轴的快速估计方法及其应用[J]. 电子与信息学报, 2010, 32(3): 548-553. doi: 10.3724/SP.J.1146.2009.00211
Bai Lu, Cao Fang, Hong Wen. Fast Approach to Estimate the Longest Axis in Coherence Region and Its Applications[J]. Journal of Electronics & Information Technology, 2010, 32(3): 548-553. doi: 10.3724/SP.J.1146.2009.00211
Citation: Bai Lu, Cao Fang, Hong Wen. Fast Approach to Estimate the Longest Axis in Coherence Region and Its Applications[J]. Journal of Electronics & Information Technology, 2010, 32(3): 548-553. doi: 10.3724/SP.J.1146.2009.00211

相干区域长轴的快速估计方法及其应用

doi: 10.3724/SP.J.1146.2009.00211

Fast Approach to Estimate the Longest Axis in Coherence Region and Its Applications

  • 摘要: 相干区域的长轴作为复相干系数间最长的连线,反映了复干涉相干系数线性变化趋势,因此在基于相干散射模型的极化干涉森林应用中具有重要研究意义。针对遍历搜索方法估计长轴效率低、精度受采样间隔限制的不足,该文提出一种相干区域长轴的快速估计方法。该方法将相干区域外切矩形的切点连线作为长轴的初始估计,利用逼近技术求解长轴。为提高森林高度估计性能,该文还提出一种应用长轴信息反演森林参数的方法。松树林的极化干涉仿真数据的结果表明,快速估计方法运行时间远低于遍历搜索方法,且长轴估计精度高于遍历搜索方法,利用长轴信息估计的森林高度更接近于仿真森林高度。
  • 郭华东, 李新武, 王长林, 等. 极化干涉雷达遥感机制及其作用[J]. 遥感学报, 2002, 6(6): 401-405.Guo Hua-dong, Li Xin-wu, and Wang Chang-lin, et al.. Themechanism and role of polarimetric SAR interferometry[J].Joural of Remote Sensing, 2002, 6(6): 401-405.[2]陈兵, 徐绍剑, 张平. 单基线PolInSAR 反演算法研究[J].电子与信息学报.2008, 30(7):1744-1746浏览[3]Papathanassiou K P, Kugler F, and Lee S, et al.. Recentadvances in polarimetric SAR interferometry for forestparameter estimation[C]. IEEE Radar Conference, Rome,Italy, 26-30 May, 2008: 1-6.[4]Papathanassiou K P and Cloude S R. Single-baselinepolarimetric SAR interferometry[J].IEEE Transactions onGeoscience and Remote Sensing.2001, 39(11):2352-2363[5]Zhou Guang-yi, Xiong Tao, and Yang Jian, et al.. Forestheight inversion based on polarimetric SAR inteferometry[C].International Conference on Signal Processing, Beijing,China, 26-29 October, 2008: 2473-2476.[6]Cloude S R and Papathanassiou K P. Three-stage inversionprocess for polarimetric SAR interferometry[J].IEEProceedings Radar, Sonar and Navigation.2003, 150(3):125-134[7]Tobias M, Florian K, and Papathanassiou K P, et al.. Forestand the random volume over ground-nature and effect of 3possible error types[C]. European Conference on SyntheticAperture Radar Proceedings, Dresden, Germany, 16-18 May2006: 13, 4.[8]Tan Lu-lu and Yang Ru-liang. Investigations on tree heightretrieval with polarimetric SAR interferometry[C].Geosciences and Remote Sensing Symposium, Boston, USA,7-11 July, 2008: 546-549.[9]Mark T, Jeffreu O, and Thomas F, et al.. Phase diversity: Adecomposition for vegetation parameter estimation usingpolarimetric SAR Interferometry[C]. European Conferenceon Synthetic Aperture Radar. Cologne, Germany, 4-6 June2002: 721-724.[10]Maxim N, Andreas R, and Ferro-Famil L. Pol-InSARcoherence set theory and application[C]. EuropeanConference on Synthetic Aperture Radar Proceedings,Dresden, Germany, 16-18 May 2006, ID. 037.[11]Maxim N, Andreas R, and Ferro-Famil L. Data classificationbased on PolInSAR coherence shapes[C]. Geosciences andRemote Sensing Symposium, Soul, Korea, 25-29 July 2005:4852-4855.[12]Laurent F, Florian K, and Eric P, et al.. Forest mapping andclassification at L band using Pol-InSAR optimal coherenceset statistics[C]. European Conference on Synthetic ApertureRadar Proceedings, Dresden, Germany, 16-18 May 2006, ID.207.[13]Pottier E, Ferro-Famil L, and Allain S, et al.. PoLSARprov3.3: The versatile educational toolbox for polarimetric andinterferometric SAR data processing[C]. Geosciences andRemote Sensing Symposium, Boston, USA, 7-11 July, 2008:471-474.
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出版历程
  • 收稿日期:  2009-02-20
  • 修回日期:  2009-04-28
  • 刊出日期:  2010-03-19

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