本文關(guān)鍵詞： 極化合成孔徑雷達 極化分解 散射機制 散射模型 散射機制分類　出處：《武漢大學》2014年博士論文　論文類型：學位論文

【摘要】：作為一種主動遙感方式,極化合成孔徑雷達(PolSAR)具有全天時全天候工作能力,其分辨率一般高于普通的真實孔徑雷達。最近幾年,它開始在軍事,測繪,農(nóng)業(yè),林業(yè),地質(zhì)等領(lǐng)域得到廣泛應(yīng)用。作為一種從PolSAR中提取信息的重要方法,極化分解,尤其是基于模型的非相干極化分解,是最近幾年P(guān)olSAR領(lǐng)域內(nèi)最活躍的方向之一。它可以獲得不同散射機制的功率和其它參數(shù),進而用于PolSAR影像分類,干涉SAR,極化相干斑濾波,土壤粗糙度和濕度估計等。 自從Freeman和Durden提出三分量分解法后到現(xiàn)在,研究人員已經(jīng)提出了二十多種基于模型的非相干分解法。這些方法雖然得到了不少成功的應(yīng)用,但是普遍存在諸如不滿足特征值非負約束,出現(xiàn)負功率值,高估體散射功率,對極化信息利用不充分,對地面散射采用相干散射模型進行模擬,不能描述去極化,以及難以有效區(qū)分森林和分布方向不平行于SAR方位向的建筑物等問題。一般采用真實數(shù)據(jù)對分解法進行驗證,缺乏和真值的比較,難以定量評估分解法對各分量功率及其它參數(shù)估計的準確度。 針對上述問題,本文首先創(chuàng)建了一個基于極化分解的模擬框架,模擬不同分量的參數(shù),計算它們不基于反射對稱的散射模型,得到功率加權(quán)后的相干矩陣。通過對模擬得到的相干矩陣利用不同方法進行分解,可以將分解結(jié)果與模擬參數(shù)進行定量比較。作者還挑選了不同散射機制主導的模擬數(shù)據(jù),以更好地模擬真實情況。 本文提出了兩種高度自適應(yīng)的分解法。這兩種方法都進行去方位處理,應(yīng)用特征值非負約束到螺旋散射和體散射參數(shù)的計算,采用Neumann自適應(yīng)散射模型和雙極子來描述體散射,選擇能讓體散射解釋最多交叉極化功率的參數(shù)作為最優(yōu)的體散射參數(shù)。但是第一種分解法不基于反射對稱計算體散射參數(shù)(簡稱為RAVD),導致在一般情況下,螺旋散射和體散射不能解釋所有的交叉極化功率。為此,采用Neumann模型描述主導地面散射以解釋剩余的交叉極化功率,采用相干模型描述次要地面散射。而第二種分解法計算體散射參數(shù)時基于反射對稱假設(shè)(簡稱為RSVD),使得在大部分以表面或雙次散射為主的區(qū)域,體散射和螺旋散射能解釋全部的交叉極化功率,再由van Zyl分解即可獲得表面散射和雙次散射的參數(shù)。但是在部分森林地區(qū),少部分交叉極化功率不能由體散射和螺旋散射解釋。在這種情況下,對觀測到的相干矩陣進行三分量分解,其中體散射和主導地面散射均由反射對稱的Neumann模型描述。如果上述分解不能取得合理結(jié)果,則進行三分量反射不對稱分解。 利用模擬數(shù)據(jù)和UAVSAR數(shù)據(jù)所做的實驗表明,在絕大多數(shù)情況下,這兩種方法可以匹配除T13外其它觀測到的相干矩陣中的元素。如果進行三分量反射不對稱分解,則有可能匹配除T13虛部之外的其它相干矩陣元素。RSVD避免了負功率值的出現(xiàn),而RAVD的結(jié)果中,負功率值的比例也低于0.070%。兩種分解法明顯降低了對體散射功率的高估,估計各分量功率的準確度高于幾種最新的特征值非負分解法。在大多數(shù)情況下,RAVD估計不同分量的方位角隨機度和復散射系數(shù)的準確度優(yōu)于RSVD,而且在它的結(jié)果中,森林和延伸方向不平行于SAR方位向的建筑物可以較為容易地區(qū)分開。但是在以表面散射或雙次散射為主的區(qū)域,RSVD估計各分量功率的效果優(yōu)于RAVD. 本文還提出了一種基于功率的非監(jiān)督散射機制分類法。散射機制類被定義為不同主導和次要散射機制的組合。通過分析不同散射機制的特征以及兩種散射機制混合時的特征,作者給出了一種基于極化特征和特征域分割的散射機制分類法。由于該分類法基本不依賴于極化分解,所以避免了對體散射功率的高估和特征值分解。該分類法的效率大大高于Wishart-H/alpha法和模糊H/alpha法,而且能夠提供次要散射機制的類別。該方法可以用于PolSAR影像的快速分類,其分類結(jié)果可以作為更復雜的分類器的初始分類。它還可能用于簡化基于模型的非相干分解。 在利用模擬數(shù)據(jù)的實驗中,該方法給出的Kappa系數(shù)為0.864。該方法識別主導散射機制的效果顯著優(yōu)于H/alpha法,Wishart-H/alpha法和模糊H/alpha法。UAVSAR數(shù)據(jù)的實驗表明,該方法能夠有效識別森林和城區(qū)的主導和次要散射機制。
[Abstract]:As an active remote sensing method, polarimetric synthetic aperture radar (PolSAR) has the ability to work all day long during the entire time, the resolution is generally higher than the ordinary real aperture radar. In recent years, it began in the military, mapping, agriculture, forestry, geology and other fields has been widely used. As an important method to extract information from in PolSAR polarization decomposition, especially non coherent polarization decomposition based on the model, the direction of recent years is one of the most active in the field of PolSAR. It can obtain different power scattering mechanisms and other parameters, and then used for PolSAR image classification, SAR interference, coherent speckle filtering, soil roughness and humidity estimation.
Since Freeman and Durden proposed the three component decomposition method after up to now, researchers have proposed more than 20 kinds of non coherent decomposition method based on model. Although these methods have many successful applications, but generally does not meet the problems such as the eigenvalue of nonnegative constraints, the negative power value, overestimate the volume scattering power of polarization information do not use fully, to simulate the ground scattering by coherent scattering model, can describe depolarization, and it is difficult to effectively distinguish between the forest and the distribution direction is not parallel to the SAR direction to the building and other issues. Generally used to verify the decomposition method for the real data, and lack of true value, it is difficult to quantitatively evaluate decomposition method to estimate each component power and other parameters accurately.
Aiming at the above problems, this paper firstly creates a simulation framework based on polarization decomposition, the simulation parameters of different components, they are not based on the calculated scattering model of reflection symmetry, get the power weighted coherent matrix. Through the decomposition of the correlation matrix by using different method of simulation, we can decompose and simulation results quantitatively comparison. The author also selected the simulated data leading to different scattering mechanisms, in order to better simulate the real situation.
This paper presents two kinds of highly adaptive decomposition method. The two methods are carried out to the azimuth processing, using eigenvalue calculation non negative constraints to spiral scattering and volume scattering parameters, using Neumann scattering model and adaptive dipoles to describe the scattering, scattering can make selection interpretation parameters of cross polarization power as the most the optimal parameters of the scattering body. But the first decomposition method is not based on computational reflection symmetry scattering parameter (RAVD), resulting in general, spiral scattering and volume scattering cannot explain cross polarization power of all. Therefore, the Neumann model was adopted to describe the dominant ground scattering to explain cross polarization power surplus, by using the coherent model describe the secondary land scattering. And second kinds of decomposition method based on the assumption of computational reflection symmetry scattering parameters (referred to as RSVD), which in most of the surface or double scattering. The area, volume scattering and spiral scattering can explain cross polarization power all the parameters can be obtained by van Zyl decomposition of surface scattering and double scattering. But in the part of the forest area, a small part of the cross polarization power cannot be explained by scattering and spiral scattering. In this case, the coherent matrix are observed three component decomposition, the scattering and scattering are dominant ground described by Neumann model. If the reflection symmetry decomposition cannot obtain reasonable results, then decompose the three component reflection asymmetry.
Using the simulated data and the UAVSAR data experiments show that, in most cases, these two methods, except T13 observed coherent matrix elements. If the three components of the reflection asymmetric decomposition, it may, in addition to T13, the imaginary part of other coherent matrix elements to avoid the emergence of negative.RSVD the power value, and the result of RAVD, the negative power value was lower than two 0.070%. decomposition method significantly reduces the overestimation of the volume scattering power, the power component estimation accuracy is higher than some of the new non negative eigenvalue decomposition method. In most cases, the RAVD estimates for different azimuth angle random component and the complex scattering coefficient is more accurate than RSVD, but also in its results, the forest and the extension direction is not parallel to the SAR direction to the building can be separated more easily. But in the area of surface scattering or double In the region where the scattering is dominant, RSVD estimates the power of each component is better than that of RAVD.
This paper also presents an unsupervised classification method based on scattering mechanism of power. A combination of scattering mechanisms are defined for different class leading and secondary scattering mechanism. Through the analysis of characteristics of different scattering mechanisms and characteristics of two kinds of scattering mechanisms are mixed, the author gives a classification of scattering mechanism features and domain segmentation based on the method. Because the basic classification does not depend on the polarization decomposition, so to avoid overestimation of the volume scattering power and eigenvalue decomposition. The classification efficiency is much higher than the Wishart-H/alpha method and fuzzy H/alpha method, and can provide a secondary scattering mechanism category. The method can be used for fast classification of PolSAR image classification, initial classification the results can be used as more complex classifiers. It may also be used to simplify the non coherent decomposition based on model.
In the experiments, the Kappa coefficient method is presented for the 0.864. method to identify the dominant scattering mechanism of the effect is much better than the H/alpha method, Wishart-H/alpha method and experiment show that the fuzzy H/alpha method of.UAVSAR data, this method can effectively identify the dominant forest and urban areas and the secondary scattering mechanism.

【學位授予單位】：武漢大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：P237;P225.1

【參考文獻】

相關(guān)期刊論文 前10條

1 李宗謙,馮孔豫;從雷達后向散射系數(shù)反演土壤濕度與復介電常數(shù)[J];中國科學E輯:技術(shù)科學;1997年03期

2 朱安寧;吉麗青;張佳寶;信秀麗;劉建立;劉恒柏;;不同類型土壤介電常數(shù)與體積含水量經(jīng)驗關(guān)系研究[J];土壤學報;2011年02期

3 張海劍;楊文;鄒同元;孫洪;;基于四分量散射模型的多極化SAR圖像分類[J];武漢大學學報(信息科學版);2009年01期

4 鄒同元;楊文;代登信;孫洪;;一種新的極化SAR圖像非監(jiān)督分類算法研究[J];武漢大學學報(信息科學版);2009年08期

5 巫兆聰;歐陽群東;胡忠文;;應(yīng)用分水嶺變換與支持向量機的極化SAR圖像分類[J];武漢大學學報(信息科學版);2012年01期

6 楊杰;史磊;李平湘;;基于極化相干最優(yōu)與極化總功率的Wishart-H/Alpha分類[J];武漢大學學報(信息科學版);2012年01期

7 何楚;劉明;許連玉;劉龍珠;;利用特征選擇自適應(yīng)決策樹的層次SAR圖像分類[J];武漢大學學報(信息科學版);2012年01期

8 牛朝陽 ,馬德寶 ,張向峰;SAR目標極化分解方法研究[J];微計算機信息;2005年23期

9 ;Polarimetric whitening filter for POLSAR image based on subspace decomposition[J];Journal of Systems Engineering and Electronics;2008年06期

10 史磊;李平湘;楊杰;;極化方位角對Yamaguchi參數(shù)分解的影響[J];中國圖象圖形學報;2011年11期

，

本文編號：1442334