Topography and Tree Height Estimation Based on the Best Normal Matrix Approximation for PolInSAR Coherence Region
SUN Ningxiao① WU Qiongzhi② Sun Lin②
①(School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China) ②(School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China)
The inversion of topography and tree height in forest area is one of the most important applications in the Polarimetric SAR Interferometry (PolInSAR). In this paper, the coherent region of the PolInSAR data is modeled and the best normal matrix is used to approximate the cross correlation matrix, further, the whitened interferometric cross-correlation matrix is obtained. The coherence region of the whitened interferometric cross-correlation matrix is a straight line. Two arbitrary coherences obtained under two different polarization states can be applied to fitting a straight line. Based on the fitting line, the topographical phase can be estimated successfully. Referring to the relationship between the volume scattering and the tree height, look-up table method is used to search the correct tree height. The proposed method can avoid the complex steps of the traditional method, which needs to solve all the coherences under different polarization states to obtain the edge of the coherent region. The proposed method simplifies the inversion procedure and improves the efficiency of inversion, meanwhile, achieves the correct topography as well as the tree height. Finally, the simulation data are applied to validating the validity and reliability of the proposed method.
孙宁霄,吴琼之*,孙林. 基于PolInSAR相干区域的最优正规矩阵近似解的地形与树高估计[J]. 电子与信息学报, 2017, 39(5): 1051-1057.
SUN Ningxiao, WU Qiongzhi, Sun Lin. Topography and Tree Height Estimation Based on the Best Normal Matrix Approximation for PolInSAR Coherence Region. JEIT, 2017, 39(5): 1051-1057.
WU Yirong, HONG Wen, and WANG Yanping. The current status and implications of polarimetric SAR interferometry[J]. Journal of Electronics & Information Technology, 2007, 29(5): 1258-1262.
[2]
GUO S L, LI Y, HONG W, et al. Modification of polarimetric SAR interferometry target decomposition with accuracy topography[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1476-1480. doi: 10.1109/ LGRS.2015.2409311.
[3]
GUO S L, LI Y, YIN Q, et al. Estimation of ground topography under forests with polarimetric SAR interferometry[C]. 2015 IET Radar Conference, Hangzhou, China, 2015: 1-6.
[4]
CLOUDE S R and PAPATHANASSIOU K P. Three-stage inversion process for polarimetric SAR interferometry[J]. IEE Proceedings-Radar, Sonar and Navigation, 2003, 150(3): 125-134. doi: 10.1049/ip-rsn:20030449.
[5]
GARESTIER F, DUBOIS-FERNANDEZ P C, and PAPATHANASSIO K P. Pine forest height inversion using single-pass X-band PolInSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(1): 59-68. doi: 10.1109/TGRS.2007.907602.
GUO Shenglong, LI Yang, HONG Wen, et al. Vertical parameters estimation of forest with compact polarimetric SAR interferometric data[J]. Journal of Electronics & Information Technology, 2016, 38(1): 71-79. doi: 10.11999/ JEIT150394.
[7]
METTE T. Forest biomass estimation from polarimetric SAR interferometry[D]. [Ph.D. dissertation], Germany: Technische Universitaet Muenchen, 2006.
FENG Qi, CHEN Erxue, LI Zengyuan, et al. Forest above-ground biomass estimation method for rugged terrain based on airborne P-band PolSAR data[J]. Scientia Silvae Sinicae, 2016, 52(3): 10-22. doi: 10.11707/j.1001-7488.20160302.
[9]
TREUHAFT R N and SIQUEIRA P R. The vertical structure of vegetated land surfaces from interferometric and polarimetric radar[J]. Radio Science, 2000, 35(1): 141-177. doi: 10.1029/1999RS900108.
DING Keliang, SHEN Yunzhong, and OU Jikun. Methods of line-fitting based on total least squares[J]. Journal of Liaoning Technical University (Natural Science), 2010, 29(1): 44-47. doi: 1008-0562(2010)01-0044-04.
XU Liying, LI Shiqiang, DENG Yunkai, et al. Improved three-stage algorithm of forest height retrieval with PolInSAR[J]. Journal of Radars, 2014, 3(1): 28-34. doi: 10.3724/SP.J.1300.2014.13089.
[12]
YI C, YAMAGUCHI Y, YAMADA H, et al. PolInSAR coherence region modeling and inversion: The best normal matrix approximation solution[J]. IEEE Transactions on Geoscience Remote Sensing, 2015, 53(2): 1048-1060. doi: 10.1109/TGRS.2014.2332553.
[13]
CLOUDE S R. Polarisation: Applications in Remote Sensing[M]. Oxford University Press, 2009.
[14]
ZACHLIN P F and HOCHSTENBACH M E. On the numerical range of a matrix[J]. Linear Multilinear Algebra, 2008, 56(1): 185-225.
[15]
POTTIER E. ProSARPro_v4.2.0[OL]. https://earth.esa. int/web/polsarpro, 2012.
