本文選題：極化干涉SAR + 目標分解　； 參考：《哈爾濱工業(yè)大學》2014年博士論文

【摘要】：森林是人類賴以生存的重要因素。它們不僅支撐著像自然界氣候變化和水循環(huán)這樣的生態(tài)規(guī)律，也為人類提供必要的自然產(chǎn)品，例如木材、食物，家畜的飼料和藥材等。因此，利用遙感技術對大面積的森林區(qū)域進行監(jiān)測和管理成為了熱點問題，而且人們對這一問題進行了大量研究。極化SAR干涉測量是現(xiàn)代遙感技術的一個重要分支。該技術結合了極化SAR和干涉SAR這兩種獨立的雷達技術。極化干涉SAR (PolInSAR)不僅對散射體的形狀和方位不敏感，而且對散射體的位置和空間分布也不敏感。此外，利用PolInSAR圖像對森林高度等森林參數(shù)進行提取是SAR圖像解譯和應用的熱點，并且在理論上和實際應用上都具有重要意義。為了提高PolInSAR圖像森林高度估計的精度，本文研究了基于PolInSAR極化信息與干涉信息的PolInSAR森林參數(shù)反演模型, PolInSAR目標分解(TD)和對森林參數(shù)的提取。 首先，對目標散射特性和典型森林參數(shù)的反演模型已經(jīng)有了很深的研究，例如隨機體-地表散射和ESPRIT。在現(xiàn)有森林高度估計方法的理論和應用基礎上，本文針對PolInSAR圖像，提出了一種提高森里高度估計精度的方法，該方法結合了總最小二乘直線擬合和ESPRIT兩種典型的處理方法。并且利用ALOS\PALSAR測量的馬來西亞地區(qū)L波段全極化干涉數(shù)據(jù)和PolSARProSim軟件仿真的數(shù)據(jù)對該方法進行了驗證。實驗結果表明，本文提出的方法可以顯著提高森林高度參數(shù)的估計準確性. 其次，本文研究了基于散射模型的PolInSAR非相干目標的目標分解方法，提出了一種應用于PolInSAR圖像森林高度估計的自適應分解模型(AMBD)。該模型把每種干涉的十字相關表示成奇次散射偶次散射和體散射的總和，不僅可以反演森林參數(shù),而且可以得到每種散射過程的權重。除此之外，該模型還利用了協(xié)方差矩陣的所有信息，這是在之前的基于模型的目標分解方法中未能實現(xiàn)的。本文利用SIR-C/X-SAR PolInSAR圖像進行了算法驗證與性能檢驗。實驗發(fā)現(xiàn)，與利用三層反演方法得到的森林高度估計結果比較，這種基于自適應模型目標分解的森林高度估計方法具有更高的精度。 再次，本文還提出了基于通用三層散射模型(GTLSM)的PolInSAR圖像森林高度估計方法。在GTLSM中，林冠頂層的相關參數(shù)可以利用AMBD估計出來，而樹干與地表的參數(shù)提取則是一個基于非線性組優(yōu)化的問題。這個模型根據(jù)極化特征和相干性的差異，把森林模型分離成三層：地表層,樹干層和樹冠層。GTLSM同樣利用SIR-C/X-SAR的L波段PolInSAR圖像進行驗證。實驗結果表明，本文提出的GTLSM可以更準確的反演森林參數(shù)。 最后，本文提出了兩種從PolInSAR圖像中估計斜坡上森林高度的方法。第一種方法是基于一般模型的目標分解方法(GMBD)，在這個方法中，我們提出了一個用隨機程度和方位角均值這兩個參數(shù)描述的一般體散射模型，這兩個未知的森林參數(shù)可以用非線性最小二乘優(yōu)化得到。這種方法不僅可以反演森林參數(shù)，，也可以反演出每種散射過程的權重，同時還針對十字交叉極化和非對角的情況，通過分離一般奇次散射與偶次散射模型各自的方位角，改進了這兩個模型。第二種方法是改進的三層散射模型方(MTLSM)。三層散射模型方法假設在斜坡地勢上，可以將森林在垂直方向上分為三層：樹冠頂層，樹干層和地表層。這三層會同時影響到三種散射過程（體散射,表面散射和偶次散射）對斜坡上森林區(qū)域的作用。該方法也介紹了PolInSAR相干信息對森林高度,平均消減，特別是局部地勢坡度等參數(shù)的影響。第二種方法不僅能夠反演出斜坡上森林的參數(shù)，而且其對地表相位與樹冠層相位的估計具有更強的魯棒性和明確性。本文將這兩種方法應用于ALOS/PALSAR測量的印度尼西亞地區(qū)L-波段圖像，實驗結果顯示了這兩種方法的有效性。
[Abstract]:Forests are important factors for human survival. They not only support the ecological laws such as natural climate change and water circulation, but also provide the necessary natural products for human beings, such as wood, food, livestock feed and medicinal materials. Therefore, the monitoring and management of large surface forest areas by remote sensing technology has become a hot spot. Problem, and people have done a lot of research on this problem. Polarization SAR interferometry is an important branch of modern remote sensing technology. This technique combines two independent radar technologies, polarizing SAR and interference SAR. Polarization interference SAR (PolInSAR) is not only insensitive to the shape and square of the scatterer, but also the position and space of the scatterer. In addition, the extraction of forest parameters such as forest height, such as the PolInSAR image, is a hot spot in the interpretation and application of SAR images, and it is of great significance both in theory and in practice. In order to improve the accuracy of the estimation of the height of the PolInSAR image forest, this paper studies the Pol based on the PolInSAR polarization information and the interference information. InSAR forest parameter inversion model, PolInSAR target decomposition (TD) and extraction of forest parameters.
First, a deep study of the scattering characteristics of the target and the model of the typical forest parameters has been deeply studied. For example, the theory and application of the random body surface scattering and the theory and application of the method of estimating the height of the forest height by ESPRIT., a method to improve the accuracy of the estimation of the height of the height of the PolInSAR is proposed in this paper. Two typical methods of small second linear fitting and ESPRIT are used. The method is verified by the data of L band full polarization interference data in the L band and the data simulated by PolSARProSim software measured by ALOSPALSAR. The experimental results show that the method proposed in this paper can significantly improve the accuracy of the estimation of the height parameters of the forest.
Secondly, the objective decomposition method of PolInSAR incoherent target based on scattering model is studied. An adaptive decomposition model (AMBD) applied to the estimation of forest height in PolInSAR images is proposed. This model represents the cross correlation of each interference into the summation of odd scattering even scattering and body scattering. It can not only invert the forest parameters. Moreover, the weight of each scattering process can be obtained. In addition, the model also uses all the information of the covariance matrix, which is not realized in the previous model based method of target decomposition. This paper uses the SIR-C/X-SAR PolInSAR image to carry out the algorithm verification and the ability test. Experimental discovery, and the use of three layer inversion method Compared with the results of forest height estimation, the forest height estimation method based on adaptive model decomposition has higher accuracy.
Thirdly, a forest height estimation method for PolInSAR images based on the general three layer scattering model (GTLSM) is proposed. In GTLSM, the correlation parameters of the canopy top layer can be estimated by AMBD, and the parameter extraction of the tree trunk and the surface is a nonlinear group optimization problem. The forest model is separated into three layers: the surface layer, the tree trunk layer and the tree crown.GTLSM also use the SIR-C/X-SAR L band PolInSAR image to verify. The experimental results show that the proposed GTLSM can more accurately retrieve the forest parameters.
Finally, this paper proposes two methods to estimate the height of the forest on the slope from the PolInSAR image. The first method is based on the general model's target decomposition method (GMBD). In this method, we propose a general body scattering model, which is described by the two parameters of the random degree and the azimuth mean, the two unknown forest parameters. This method can be obtained by nonlinear least squares optimization. This method can not only inverse the forest parameters, but also reverse the weight of each scattering process. At the same time, the two models are improved by separating the azimuth of the general odd scattering and the even scattering models for the cross and non diagonal conditions. Second methods are improved. It is an improved three layer scattering model square (MTLSM). The three layer scattering model assumes that in the slope terrain, the forest can be divided into three layers in the vertical direction: the top of the tree crown, the trunk layer and the surface layer. The three layers will simultaneously affect the effect of the three scattering processes (body scattering, surface scattering and even scattering) on the forest area on the slope. The method also introduces the influence of PolInSAR coherent information on forest height, average reduction, especially local terrain slope. The second methods can not only reverse the parameters of the forest on the slope, but also have stronger robustness and clarity to the estimation of the surface phase and the phase of the tree crown. In this paper, these two methods are applied in the paper. LSAR measured L- band images in Indonesia area. The experimental results show the effectiveness of the two methods.

【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN957.52

【參考文獻】

相關期刊論文 前1條

1 李新武,郭華東,廖靜娟,王長林,閻福禮;航天飛機極化干涉雷達數(shù)據(jù)反演地表植被參數(shù)[J];遙感學報;2002年06期

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