L波段PolInSAR图像地表参数反演方法研究
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摘要
极化干涉合成孔径雷达(PolInSAR)集PolSAR和InSAR测量技术于一体,可以同时把目标的精细物理特征与空间分布特性结合起来。极化干涉测量不仅能够提高常规InSAR测量的精度,而且有助于更好的理解目标的散射机理和所发生的散射过程,进一步拓展了SAR在成像遥感领域的应用,在军事和民用领域具有无法估量的作用。
PolInSAR可用于反演地表植被参数,特别是森林的高度参数。地表参数的提取为地表分类及进一步目标识别奠定了基础,因此用极化干涉雷达数据提取地表植被参数是当前极化干涉研究的热点问题。
论文主要研究了基于遗传算法的PolInSAR图像地表植被参数反演算法。主要内容包括:极化干涉相干最优理论,相干散射模型和反演模型以及基于遗传算法的参数反演过程。
本文首先对PolSAR的基本工作原理进行了阐述,包括目标极化散射特性的表征和基本极化散射机理。进而对InSAR的基本工作原理以及影响干涉数据相干性的各种因素进行了分析。然后,对矢量干涉和极化干涉相干最优理论进行了系统的研究和探讨。
接下来,论文讨论了矢量相干散射模型,重点研究了RVoG相干散射模型和相应的参数反演模型,并分析了模型中各个参数对干涉相干性的影响。在对PolInSAR理论进行深入讨论后,论文对参数反演所使用的遗传算法基本理论进行了研究,分别讨论了基本遗传算法和两代竞争遗传算法的优劣以及实现步骤。在此基础上,通过遗传算法对参数反演过程进行优化,最终得到地表参数。
最后,论文给出了利用SIR-C/X-SAR L波段两组真实数据的反演结果,并分析了不同遗传算法及不同参数对反演过程的影响。实验结果表明论文所采用的算法具有一定的有效性和准确性。
Polarimetric SAR interferometry (PolInSAR) is the technique that integrates radar polarimetry and radar interferometry. The interferometric analysis of a full coherent polarimetric data set enables the combination of final structure properties and spatial information of the targets. PolInSAR is a potential technique that not only improves the accuracy of the interferometric measurements but also allows a more sophisticated physical interpretation of scattering mechanism. With lots of advantages in many areas of military, civil and scientific researches, PolInSAR has extented the applications of SAR remote sensing, and has wide and latent prospective applications and valuable researching aspects.
PolInSAR has been used to invert vegetation parameters of terrain surface, especially for height of forest. These parameters are the bases of terrain classification and target detection and recognition. Therefore, terrain surface vegetation parameters inversion using PolInSAR data has been hot research topics in polarimetric interferometry in recent years.
Terrain surface vegetation parameters inversion using PolInSAR data based on Genetic Algorithm (GA) is discussed in detail in this dissertation. The main contents include Polarimetric Interferometric Phase Coherence Optimization (PIPCO); Random Volume over Ground (RVoG) coherence scattering model and corresponding inversion model; optimization of inversion procedure using GA.
Firstly, this dissertation provides an overview of basic theory of PolSAR, such as representation of polarimetric scattering characters of targets and elementary scattering mechanisms. Then theory of InSAR and all kinds of decorrelation factors are discussed. Furthermore, the principles of vector interferometry and the theory of PIPCO are discussed comprehensively.
Secondly, vector coherence scattering models are studied. The study is focused on the RVoG scattering model and corresponding inversion model. The influences upon coherence of all the parameters in the model are discussed.
Thirdly, after in-depth discussion of theory of PolInSAR, basic theory of GA used in the inversion procedure is studied. The performaces and processes of
PolInSAR可用于反演地表植被参数,特别是森林的高度参数。地表参数的提取为地表分类及进一步目标识别奠定了基础,因此用极化干涉雷达数据提取地表植被参数是当前极化干涉研究的热点问题。
论文主要研究了基于遗传算法的PolInSAR图像地表植被参数反演算法。主要内容包括:极化干涉相干最优理论,相干散射模型和反演模型以及基于遗传算法的参数反演过程。
本文首先对PolSAR的基本工作原理进行了阐述,包括目标极化散射特性的表征和基本极化散射机理。进而对InSAR的基本工作原理以及影响干涉数据相干性的各种因素进行了分析。然后,对矢量干涉和极化干涉相干最优理论进行了系统的研究和探讨。
接下来,论文讨论了矢量相干散射模型,重点研究了RVoG相干散射模型和相应的参数反演模型,并分析了模型中各个参数对干涉相干性的影响。在对PolInSAR理论进行深入讨论后,论文对参数反演所使用的遗传算法基本理论进行了研究,分别讨论了基本遗传算法和两代竞争遗传算法的优劣以及实现步骤。在此基础上,通过遗传算法对参数反演过程进行优化,最终得到地表参数。
最后,论文给出了利用SIR-C/X-SAR L波段两组真实数据的反演结果,并分析了不同遗传算法及不同参数对反演过程的影响。实验结果表明论文所采用的算法具有一定的有效性和准确性。
Polarimetric SAR interferometry (PolInSAR) is the technique that integrates radar polarimetry and radar interferometry. The interferometric analysis of a full coherent polarimetric data set enables the combination of final structure properties and spatial information of the targets. PolInSAR is a potential technique that not only improves the accuracy of the interferometric measurements but also allows a more sophisticated physical interpretation of scattering mechanism. With lots of advantages in many areas of military, civil and scientific researches, PolInSAR has extented the applications of SAR remote sensing, and has wide and latent prospective applications and valuable researching aspects.
PolInSAR has been used to invert vegetation parameters of terrain surface, especially for height of forest. These parameters are the bases of terrain classification and target detection and recognition. Therefore, terrain surface vegetation parameters inversion using PolInSAR data has been hot research topics in polarimetric interferometry in recent years.
Terrain surface vegetation parameters inversion using PolInSAR data based on Genetic Algorithm (GA) is discussed in detail in this dissertation. The main contents include Polarimetric Interferometric Phase Coherence Optimization (PIPCO); Random Volume over Ground (RVoG) coherence scattering model and corresponding inversion model; optimization of inversion procedure using GA.
Firstly, this dissertation provides an overview of basic theory of PolSAR, such as representation of polarimetric scattering characters of targets and elementary scattering mechanisms. Then theory of InSAR and all kinds of decorrelation factors are discussed. Furthermore, the principles of vector interferometry and the theory of PIPCO are discussed comprehensively.
Secondly, vector coherence scattering models are studied. The study is focused on the RVoG scattering model and corresponding inversion model. The influences upon coherence of all the parameters in the model are discussed.
Thirdly, after in-depth discussion of theory of PolInSAR, basic theory of GA used in the inversion procedure is studied. The performaces and processes of
引文
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8 S.R.Cloude, K.P.Papathanassiou. A 3-Stage Inversion Process for Polarimetric SAR Interferometry. Proc. of EUSAR’02, Cologne, Germany, 4-6 June 2002:297~282
9 S.R.Cloude, K.P.Papathanassiou. Three-stage Inversion Process for Polarimetric SAR Interferometry. IEE Proc. Radar Sonar Navig, 2003, 150(3):125~134
10 M.Qong. Polarization State Conformation and its Application to Change Detection in PolSAR. IEEE GRSL, Oct. 2004, 1(4):304~308
11 M.Qong. A New Polarization State Conformation and its Application to Coherence Optimization in PolInSAR. Proc. of IGARSS'04, Anchorage, Alaska, 20-24 Sept. 2004, 4:2495~2498
12 M.Qong. Coherence Optimization using the Polarization Conformation in PolInSAR. IEEE GRSL, July 2005, 2(3):301~305
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2 K.P.Papathanassiou, A.Reiger, S.R.Cloude. Vegetation and Ground Parameter Estimation using Polarimetric Interferometry Part I: The Role of Polarisation. Part II: Parameter Inversion and Optimal Polarisations. Proc. of the CEOS SAR Workshop, Toulouse, 26-29 Oct. 1999:347~358
3 R.N.Treuhaft, S.N.Madsen, M.Moghaddam, J.J.van Zyl. Vegetation Characteristics and Surface Topography from Interferometric Radar. Radio Science, 1996, 31:1449~1485
4 R.N.Treuhaft, S.R.Cloude. The Structure of Oriented Vegetation from Polarimetric Interferometry. IEEE Trans. on GRS, 1999, 37(5):2620~2624
5 R.N.Treuhaft. Vertical Structure of Vegetated Land Surfaces from Interferometric and Polarimetric. Radar. Radio Sciences, 2000, 35(1):141~177
6 K.P.Papathanassiou, S.R.Cloude. Single-baseline Polarimetric SAR Interferometry. IEEE Trans. on GRS, 2001, 39(11):2352~2363
7 M.Isola, S.R.Cloude. Forest Height Mapping using Spaceborne Polarimetric SAR Interferometry. Proc. of IGARSS’01, Sydney, Australia, 9-13 July 2001, 3:1095~1097
8 S.R.Cloude, K.P.Papathanassiou. A 3-Stage Inversion Process for Polarimetric SAR Interferometry. Proc. of EUSAR’02, Cologne, Germany, 4-6 June 2002:297~282
9 S.R.Cloude, K.P.Papathanassiou. Three-stage Inversion Process for Polarimetric SAR Interferometry. IEE Proc. Radar Sonar Navig, 2003, 150(3):125~134
10 M.Qong. Polarization State Conformation and its Application to Change Detection in PolSAR. IEEE GRSL, Oct. 2004, 1(4):304~308
11 M.Qong. A New Polarization State Conformation and its Application to Coherence Optimization in PolInSAR. Proc. of IGARSS'04, Anchorage, Alaska, 20-24 Sept. 2004, 4:2495~2498
12 M.Qong. Coherence Optimization using the Polarization Conformation in PolInSAR. IEEE GRSL, July 2005, 2(3):301~305
13 G.Vasile, E. Trouv′e, et al. Improving Coherence Estimation for High-Resolution Polarimetric SAR Interferometry. Proc. of IGARSS'04, Anchorage, Alaska, 20-24 Sept. 2004, 3:1796~1799
14 J.L.Gomez-Dans, S.Quegan. Constraining Coherence Optimisation in Polarimetric Interferometry of Layered Targets. Proc. of PolInSAR 2005, Frascati, Italy, 17-21 Jan. 2005:1~6
15 K.P.Papathanassiou, T.Mette, I.Hajnsek. Model Based Forest Height Estimation from Single Baseline PolInSAR Data: The Fichtelgebirge Test Case. Proc. of PolInSAR 2003, Frascati, Italy, 14-16 Jan. 2003:1~6
16 L.Sagues, X.Fabregas, A.Broquetas. Extraction of Vegetation Structure and Surface Parameters Using Polarimetric and Interferometric SAR Techniques. Proc. of IGARSS’00, Honolulu, Hawaii, 24-28 July 2000, 1:141~143
17 I.Hajnsek, K.P.Papathanassiou, A.Moreia, S.R.Cloude. Surface Parameter Estimation using Interferometric and Polarimetric SAR. Proc. of IGARSS’02, Toronto, Canada, 24-28 June 2002, 1:420~422
18 I.Hajnsek, J.L.Alvarez-Perez, K.P.Papathanassiou, A.Moreia, S.R.Cloude. Surface Parameter Estimation using Interferometric Coherences at Different Polarisations. Proc. of PolInSAR 2003, Frascati, Italy, 14-16 Jan. 2003:1~6
19 T.Flynn, M.Tabb, R.Carande. Direction Estimation of Vegetation Parameters from Covariance Data in Polarimetric SAR Interferometry. Proc. of IGARSS’02, Toronto, Canada, 24-28 June 2002, 3:1908~1910
20 M.Tabb, T.Flynn, R.Carande. Full Maximum Likelihood Inversion of PolInSAR Scattering Models. Proc. of IGARSS’04, Anchorage, Alaska, 20-24 Sept. 2004, 2:1232~1235
21 L.Ferro-Famil, A.Reigber, E.Pottier. Time-Frequency Analysis of Natural Scene Anisotropic Scattering Behavior from PolInSAR Data. Proc. of EUSAR’04, Ulm, Germany, 25-27 May 2004:1~4
22 E.Colin, W.Tabbara, A.Reigber. Polarimetric Interferometry and Time-Frequency Analysis Applied to A Urban Area at X-band. Proc. ofIGARSS’05, Seoul, Korea, 25-29 July 2005, 2:1077~1080
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