基于極化信息的SAR目標檢測鑒別方法研究
發(fā)布時間：2018-09-09 17:36
【摘要】：極化合成孔徑雷達(Polarimetric Synthetic Aperture Radar,PolSAR)利用不同極化方式交替發(fā)射與接收雷達信號,能夠獲得豐富的目標散射信息。這些信息在雷達圖像的特征提取,圖像解譯及自動目標識別(Automatic Target Recognition,ATR)技術(shù)中發(fā)揮著重要作用。因此本文從極化信息的提取與運用出發(fā),對基于極化SAR圖像的極化目標分解,基于極化信息的目標檢測與目標鑒別等方面進行了研究。本文首先簡要介紹了極化SAR目標分解,目標檢測與鑒別的研究背景與意義,概述了該課題的國內(nèi)外研究狀況,并介紹了本文研究的主要內(nèi)容。在此基礎上,對本文的主要研究內(nèi)容分以下三個方面進行詳細介紹:第一部分,主要介紹了極化處理的電磁波理論基礎,并在此基礎上對極化數(shù)據(jù)的預處理:極化定標理論進行介紹,分別研究了基于點目標的定標算法和基于分布式目標的定標算法,分析了極化相位定標,串擾定標及通道不平衡定標對極化數(shù)據(jù)的影響。第二部分,研究了極化目標分解問題,主要是非相干分解中基于模型的分解問題。該工作主要包含以下兩方面:1,介紹了幾種經(jīng)典的基于模型的分解算法,包括Freeman-Durden分解,改進的Yamaguichi分解,基于非負特征約束的模型分解,基于雙酉變換的分解等。2,提出了一種基于極化相似度匹配的極化相干矩陣散射能量提取方法,該方法利用極化相似度獲取與原始相干矩陣相似度最高的基本散射機制并對該散射機制進行半正定約束的優(yōu)先分解,避免了主導散射機制能量的低估問題,取得了較好的分解結(jié)果。第三部分,研究了基于極化信息的目標檢測及鑒別問題。在目標檢測階段,首先介紹了傳統(tǒng)的基于閾值的CFAR檢測算法,針對CFAR檢測在復雜場景下失效的情況及對極化信息運用不充分情況,研究了基于極化信息與支持向量數(shù)據(jù)描述(Support Vector Data Discriptor,SVDD)的極化檢測算法。SVDD構(gòu)造了包含單類目標數(shù)據(jù)樣本的緊致超球體邊界,使其能很好地對一類問題進行分類。通過對目標樣本提取大量極化特征,采用SVDD進行訓練,獲取一個較優(yōu)的分類界面,采用訓練得到的分類邊界對待分類樣本進行分類,獲取目標雜波二值圖,通過對二值圖像進行形態(tài)學濾波去除明顯不是目標的雜波區(qū)域,最終獲得疑似目標切片,實現(xiàn)目標檢測。在目標鑒別階段,詳細介紹了幾種經(jīng)典的鑒別特征,該特征主要是由林肯實驗室及其他實驗室提出的紋理特征。并介紹了高斯鑒別器及SVDD鑒別器的基本原理,采用兩種鑒別器實現(xiàn)了目標的鑒別。
[Abstract]:Polarimetric synthetic Aperture Radar (Polarimetric Synthetic Aperture Radar,PolSAR) can obtain abundant target scattering information by alternately transmitting and receiving radar signals in different polarization modes. This information plays an important role in feature extraction, image interpretation and automatic target recognition (Automatic Target Recognition,ATR) of radar images. Therefore, from the point of view of the extraction and application of polarization information, the polarization target decomposition based on polarimetric SAR image, the target detection and target identification based on polarization information are studied in this paper. In this paper, the research background and significance of polarimetric SAR target decomposition, target detection and identification are briefly introduced, the research status of this topic at home and abroad is summarized, and the main contents of this paper are introduced. On this basis, the main contents of this paper are described in detail as follows: in the first part, the electromagnetic wave theory of polarization processing is introduced. On this basis, the preprocessing of polarization data: polarization calibration theory is introduced. The calibration algorithm based on point target and the algorithm based on distributed target are studied, and the polarization phase calibration is analyzed. The influence of crosstalk calibration and channel imbalance calibration on polarization data. In the second part, we study the problem of polarimetric target decomposition, which is mainly model-based decomposition in incoherent decomposition. This work mainly includes the following two aspects: 1, introduces several classical model-based decomposition algorithms, including Freeman-Durden decomposition, improved Yamaguichi decomposition, model decomposition based on non-negative feature constraints. Based on the decomposition of double unitary transformation, a polarization coherence matrix scattering energy extraction method based on polarization similarity matching is proposed. This method uses polarization similarity to obtain the basic scattering mechanism which has the highest similarity with the original coherent matrix, and decomposes the scattering mechanism with positive semidefinite constraints first, thus avoiding the problem of underestimating the energy of dominant scattering mechanism. A good decomposition result is obtained. In the third part, the problem of target detection and identification based on polarization information is studied. In the phase of target detection, the traditional threshold-based CFAR detection algorithm is introduced, aiming at the failure of CFAR detection in complex scenarios and the insufficient use of polarization information. In this paper, a polarization detection algorithm based on polarization information and support vector data description (Support Vector Data Discriptor,SVDD) is studied. A compact hypersphere boundary containing a single class of target data samples is constructed so that it can classify a class of problems well. By extracting a large number of polarization features from the target samples and training with SVDD, a better classification interface is obtained. The classification boundary is used to classify the classified samples, and the binary image of the target clutter is obtained. By using morphological filtering to remove the clutter region which is obviously not the target, the suspected target slice can be obtained and the target detection can be realized. In the phase of target identification, several classical discriminant features are introduced in detail, which are mainly texture features proposed by Lincoln Lab and other laboratories. The basic principles of Gao Si discriminator and SVDD discriminator are introduced.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
本文編號：2233095
[Abstract]:Polarimetric synthetic Aperture Radar (Polarimetric Synthetic Aperture Radar,PolSAR) can obtain abundant target scattering information by alternately transmitting and receiving radar signals in different polarization modes. This information plays an important role in feature extraction, image interpretation and automatic target recognition (Automatic Target Recognition,ATR) of radar images. Therefore, from the point of view of the extraction and application of polarization information, the polarization target decomposition based on polarimetric SAR image, the target detection and target identification based on polarization information are studied in this paper. In this paper, the research background and significance of polarimetric SAR target decomposition, target detection and identification are briefly introduced, the research status of this topic at home and abroad is summarized, and the main contents of this paper are introduced. On this basis, the main contents of this paper are described in detail as follows: in the first part, the electromagnetic wave theory of polarization processing is introduced. On this basis, the preprocessing of polarization data: polarization calibration theory is introduced. The calibration algorithm based on point target and the algorithm based on distributed target are studied, and the polarization phase calibration is analyzed. The influence of crosstalk calibration and channel imbalance calibration on polarization data. In the second part, we study the problem of polarimetric target decomposition, which is mainly model-based decomposition in incoherent decomposition. This work mainly includes the following two aspects: 1, introduces several classical model-based decomposition algorithms, including Freeman-Durden decomposition, improved Yamaguichi decomposition, model decomposition based on non-negative feature constraints. Based on the decomposition of double unitary transformation, a polarization coherence matrix scattering energy extraction method based on polarization similarity matching is proposed. This method uses polarization similarity to obtain the basic scattering mechanism which has the highest similarity with the original coherent matrix, and decomposes the scattering mechanism with positive semidefinite constraints first, thus avoiding the problem of underestimating the energy of dominant scattering mechanism. A good decomposition result is obtained. In the third part, the problem of target detection and identification based on polarization information is studied. In the phase of target detection, the traditional threshold-based CFAR detection algorithm is introduced, aiming at the failure of CFAR detection in complex scenarios and the insufficient use of polarization information. In this paper, a polarization detection algorithm based on polarization information and support vector data description (Support Vector Data Discriptor,SVDD) is studied. A compact hypersphere boundary containing a single class of target data samples is constructed so that it can classify a class of problems well. By extracting a large number of polarization features from the target samples and training with SVDD, a better classification interface is obtained. The classification boundary is used to classify the classified samples, and the binary image of the target clutter is obtained. By using morphological filtering to remove the clutter region which is obviously not the target, the suspected target slice can be obtained and the target detection can be realized. In the phase of target identification, several classical discriminant features are introduced in detail, which are mainly texture features proposed by Lincoln Lab and other laboratories. The basic principles of Gao Si discriminator and SVDD discriminator are introduced.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
【共引文獻】
中國期刊全文數(shù)據(jù)庫 前5條
1 王爽;于佳平;劉坤;侯彪;焦李成;;基于雙邊濾波的極化SAR相干斑抑制[J];雷達學報;2014年01期
2 申曉華;王耀強;鄭磊;郭小靜;;Radarsat-2四極化影像在河套灌區(qū)土壤含鹽量反演中的應用[J];湖北農(nóng)業(yè)科學;2014年15期
3 陳建宏;趙擁軍;黃潔;劉偉;賴濤;;改進的多視PolSAR非局部均值濾波算法[J];測繪科學技術(shù)學報;2014年05期
4 任俊英;蘇彩霞;曹永鋒;;基于中間層特征的全極化SAR監(jiān)督地物分類[J];遙感技術(shù)與應用;2014年02期
5 楊靖;蘇彩霞;曹永鋒;;全極化SAR數(shù)據(jù)土地覆蓋分類精度分析[J];遙感信息;2015年05期
中國碩士學位論文全文數(shù)據(jù)庫 前10條
1 黃曉東;極化目標模型分解的不一致性研究[D];中國地質(zhì)大學;2013年
2 吳祥;極化SAR與可見光遙感影像融合算法研究[D];杭州電子科技大學;2014年
3 孫晨;基于子孔徑分解的極化SAR圖像分類方法研究[D];首都師范大學;2014年
4 于佳平;基于核函數(shù)的極化SAR相干斑抑制研究[D];西安電子科技大學;2014年
5 張世吉;極化SAR目標檢測算法研究及軟件設計[D];西安電子科技大學;2014年
6 杜麗娜;極化SAR/InSAR信號處理與應用[D];西安電子科技大學;2014年
7 張晶晶;基于混合塊相似性的極化SAR相干斑抑制研究[D];西安電子科技大學;2014年
8 李楊;基于Bootstrap統(tǒng)計方法的SAR圖像相干斑抑制研究[D];西安電子科技大學;2014年
9 寇杏子;結(jié)合極化特征和圖像特征的極化SAR圖像分類研究[D];西安電子科技大學;2014年
10 劉佳穎;基于粒子群優(yōu)化和Freeman分解的SAR圖像分割與分類[D];西安電子科技大學;2014年
，本文編號：2233095
本文鏈接：http://sikaile.net/kejilunwen/wltx/2233095.html
最近更新
教材專著
