森林高度的单基线P波段PolInSAR反演
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摘要
针对传统三阶段方法难以适用于P波段极化干涉合成孔径雷达(PolInSAR)植被高反演难题,该文采用先验信息对消光系数进行固定,在此基础之上,对经典三阶段算法中地体散射幅度比为0的假设进行参数化。通过模拟实验验证新方法相比已有方法在不同地体幅度比条件下均能得到较好的反演精度,且固定消光系数不会引起树高显著偏差。最后,利用2景E-SAR P波段全极化数据进行树高度反演。实验结果表明,新方法反演树高的均方根误差(RMSE)为3.43m,相比已有方法,RMSE提高的幅度为35.7%,有效提升了传统三阶段算法的适用范围。
Since the classical 3-stage inversion method is not suitable for forest height inversion from P-band polarimeric interferometry SAR(PolInSAR)data,the main idea of the proposed method was that the extinction could be fixed on the basis of the prior information with respect to the forest.Subsequently,instead of the null ground to volume ratio assumption,the ground to volume ratio which is zero in the classical 3-stage inversion method was parameterized.A simulated experiment was firstly conducted.It indicated that the new approach could be applied to invert more accurate forest height under the different ground to volume cases,moreover,the fixed extinction could not induce pronounced bias of the forest height estimation.Finally,two E-SAR P-band PolInSAR imagines were selected for exacting the forest height.The results showed that the root mean squared error(RMSE)of differences between the forest height and LiDAR-derived forest height was 3.43 m.It implied an improvement in RMSE by 35.7% in comparison to the 3-stage method.This demonstrated the new method was available.
Since the classical 3-stage inversion method is not suitable for forest height inversion from P-band polarimeric interferometry SAR(PolInSAR)data,the main idea of the proposed method was that the extinction could be fixed on the basis of the prior information with respect to the forest.Subsequently,instead of the null ground to volume ratio assumption,the ground to volume ratio which is zero in the classical 3-stage inversion method was parameterized.A simulated experiment was firstly conducted.It indicated that the new approach could be applied to invert more accurate forest height under the different ground to volume cases,moreover,the fixed extinction could not induce pronounced bias of the forest height estimation.Finally,two E-SAR P-band PolInSAR imagines were selected for exacting the forest height.The results showed that the root mean squared error(RMSE)of differences between the forest height and LiDAR-derived forest height was 3.43 m.It implied an improvement in RMSE by 35.7% in comparison to the 3-stage method.This demonstrated the new method was available.
引文
[1]付海强,朱建军,汪长城,等.极化干涉SAR植被高反演复数最小二乘平差法[J].测绘学报,2014,43(10):1061-1067.(FU Haiqiang,ZHU Jianjun,WANG Changcheng,et al.Polarimetric SAR interferometry vegetation height inversion method of complex least squares adjustment[J].Acta Geodaetica et Cartographica Sinica,2014,43(10):1061-1067.)
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[7]GARESTIER F,DUBOISFERNANDEZ P C,GUYON D,et al.Forest biophysical parameter estimation using L-and P-band polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(10):3379-3388.
[8]SEUNG-KUK L,KUGLER F,PAPATHANASSIOU K P,et al.Quantifying temporal decorrelation over BOREAL forest at L-and P-band[C]∥European Conference on Synthetic Aperture Radar.Friedrichshafen,Germany:VDE,2008:1-4.
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[10]NEUMANN M,FERRO-FAMIL L,REIGBER A.Estimation of forest structure,ground,and canopy layer characteristics from multibaseline polarimetric interferometric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(3):1086-1104.
[11]CLOUDE S R.Polarization:applications in remote sensing[M].New York:Oxford University Press,2009.
[12]GARESTIER F,TOAN T L.Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(3):1528-1539.
[2]PAPATHANASSIOU K P,CLOUDE S R.Singlebaseline polarimetric SAR interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(11):2352-2363.
[3]CLOUDE S R,PAPATHANASSIOU K P.Threestage inversion process for polarimetric SAR interferometry[J].IEE Proceedings-Radar,Sonar and Navigation,2003,150(3):125-134.
[4]KUGLER F,SCHULZE D,HAJNSEK I,et al.TanDEM-X Pol-InSAR performance for forest height estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6404-6422.
[5]HAJNSEK I,KUGLER F,LEE S,et al.Tropicalforest-parameter estimation by means of Pol-InSAR:the INDREX-II campaign[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(2):481-493.
[6]TEBALDINI S,ROCCA F.Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(1):232-246.
[7]GARESTIER F,DUBOISFERNANDEZ P C,GUYON D,et al.Forest biophysical parameter estimation using L-and P-band polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(10):3379-3388.
[8]SEUNG-KUK L,KUGLER F,PAPATHANASSIOU K P,et al.Quantifying temporal decorrelation over BOREAL forest at L-and P-band[C]∥European Conference on Synthetic Aperture Radar.Friedrichshafen,Germany:VDE,2008:1-4.
[9]FREEMAN A,DURDEN S L.A three-component scattering model for polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(3):963-973.
[10]NEUMANN M,FERRO-FAMIL L,REIGBER A.Estimation of forest structure,ground,and canopy layer characteristics from multibaseline polarimetric interferometric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(3):1086-1104.
[11]CLOUDE S R.Polarization:applications in remote sensing[M].New York:Oxford University Press,2009.
[12]GARESTIER F,TOAN T L.Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(3):1528-1539.