高分辨率SAR圖像車輛目標分形特征提取技術
發(fā)布時間：2018-11-15 07:53
【摘要】：隨著合成孔徑雷達(SAR)技術的飛速發(fā)展和載裝平臺的多樣化,戰(zhàn)場環(huán)境的實時監(jiān)測已成為可能。尤其是當SAR圖像的分辨率達到了亞分米級,已具備了戰(zhàn)場車輛目標(比如坦克、裝甲車、卡車、導彈發(fā)射車等)的快速發(fā)現(xiàn)和分類識別能力。因此,如何基于高分辨率SAR圖像實現(xiàn)車輛目標的判讀處理已經成為戰(zhàn)場信息處理中亟待解決的關鍵問題。其中,目標特征提取技術是SAR圖像車輛目標判讀處理的核心,所提取特征的可區(qū)分能力和普適性很大程度上影響SAR圖像車輛目標鑒別虛警率,以及分類準確度。本文主要針對SAR圖像車輛目標檢測過程中普遍存在的高虛警概率問題,分析車輛目標和背景地物在SAR圖像上的現(xiàn)象學差異,采用分形的理論實現(xiàn)上述差異性現(xiàn)象學特性的定量表征,并以此實現(xiàn)車輛目標和自然地物的辨別。論文綜述了分形理論的基本概念、物理性質、研究現(xiàn)狀和發(fā)展趨勢。結合SAR成像過程中其成像對象具有復雜多變的后向散射特性,以及車輛目標像素強度在SAR圖像上呈現(xiàn)出劇烈起伏的特性和較明顯的間隙尺寸,指出歐氏幾何在描述上述現(xiàn)象的局限性。并總結了基于分形理論定量表征車輛目標特征的物理量,如分形維數(shù)特征、擴展分形特征和間隙度特征。然后,論文重點研究了如何基于分形理論提取車輛目標的多種間隙度特征的問題。文中在對現(xiàn)有間隙度特征鑒別性能進行分析的基礎之上提出了二重方差間隙度特征。該特征定義盒子質量為盒子內像素幅度值的方差,再計算盒子質量的標準方差,得到整個切片內像素集合的二重方差,以此作為一種新的間隙度特征量。多種實測數(shù)據(jù)的對比分析實驗表明二重方差間隙度特征提升了對SAR圖像車輛目標的鑒別性能,具有較好的穩(wěn)定性。論文通過挖掘車輛目標和自然地物在高分辨率SAR圖像上呈現(xiàn)的顯著性差異,基于分形理論的度量方式,提出了一種高維分層間隙度特征矢量。該特征矢量能夠在不同尺度上定量描述車輛目標邊緣輪廓的間隙度和目標像素的不規(guī)則程度,用于消除目標提取過程中由自然地物產生的虛警。該特征矢量的計算過程中,首先對待測切片數(shù)據(jù)進行線性變換,統(tǒng)一其像素的灰度動態(tài)范圍;然后對其由中心向四周擴散分層,以每一層的像素幅度方差作為高維分層間隙度特征矢量的一維分量;最后用模糊C均值聚類方法將高維分層間隙度特征矢量進行聚類處理,并利用聚類隸屬度函數(shù)實現(xiàn)車輛目標與背景地物的鑒別。論文采用仿真SAR圖像數(shù)據(jù)、MSTAR數(shù)據(jù)庫和國內自主的機載SAR圖像數(shù)據(jù)測試了文中提取的多種間隙度特征的鑒別性能,并對它們進行了對比分析。
[Abstract]:With the rapid development of synthetic Aperture Radar (SAR) technology and the diversification of loading platform, real-time monitoring of battlefield environment has become possible. Especially when the resolution of SAR image reaches sub-decimeter level, it has the ability of fast detection and recognition of battlefield vehicle targets (such as tanks, armored vehicles, trucks, missile launchers, etc.). Therefore, how to realize vehicle target interpretation based on high-resolution SAR images has become a key problem in battlefield information processing. Target feature extraction technology is the core of vehicle target interpretation processing in SAR images. The distinguishing ability and universality of the extracted features greatly affect the false alarm rate and classification accuracy of vehicle target identification in SAR images. Aiming at the problem of high false alarm probability in the process of vehicle target detection in SAR images, this paper analyzes the phenomenological differences between vehicle targets and background objects on SAR images. The fractal theory is used to realize the quantitative representation of the phenomenological characteristics of the above differences and to distinguish the vehicle objects from the natural features. The basic concept, physical properties, research status and development trend of fractal theory are reviewed in this paper. Combined with the complex and variable backscattering characteristics of the object in the SAR imaging process, the pixel intensity of the vehicle object presents the characteristics of sharp fluctuation and obvious gap size on the SAR image. The limitation of Euclidean geometry in describing the above phenomena is pointed out. Based on the fractal theory, the physical quantities of vehicle target features are summarized, such as fractal dimension feature, extended fractal feature and clearance feature. Then, the paper focuses on how to extract various clearance features of vehicle targets based on fractal theory. Based on the analysis of the existing gap degree features, a double variance gap degree feature is proposed in this paper. This feature defines the box mass as the variance of the pixel amplitude in the box, then calculates the standard variance of the box mass, and obtains the double variance of the pixel set in the whole slice. The comparison and analysis of various measured data show that the characteristics of double variance gap degree can improve the performance of vehicle target identification in SAR images and have good stability. By mining the significant differences between vehicle targets and natural objects on high resolution SAR images, a new feature vector of high dimensional stratified clearance degree is proposed based on fractal theory. The feature vector can quantitatively describe the clearance degree of vehicle target edge contour and the irregularity of target pixels at different scales, and can be used to eliminate false alarm generated by natural objects in the process of target extraction. In the calculation of the feature vector, the linear transformation of the measured slice data is firstly carried out to unify the dynamic range of the gray level of the pixel. Then, the pixel amplitude variance of each layer is taken as the one dimensional component of the feature vector of high dimensional stratification gap degree. Finally, a fuzzy C-means clustering method is used to deal with the feature vectors of high dimensional stratified clearance degree, and the membership function is used to identify the vehicle objects from the background objects. In this paper, simulated SAR image data, MSTAR database and domestic independent airborne SAR image data are used to test the discriminant performance of various gap features extracted in this paper, and to compare and analyze them.
【學位授予單位】：國防科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
本文編號：2332653
[Abstract]:With the rapid development of synthetic Aperture Radar (SAR) technology and the diversification of loading platform, real-time monitoring of battlefield environment has become possible. Especially when the resolution of SAR image reaches sub-decimeter level, it has the ability of fast detection and recognition of battlefield vehicle targets (such as tanks, armored vehicles, trucks, missile launchers, etc.). Therefore, how to realize vehicle target interpretation based on high-resolution SAR images has become a key problem in battlefield information processing. Target feature extraction technology is the core of vehicle target interpretation processing in SAR images. The distinguishing ability and universality of the extracted features greatly affect the false alarm rate and classification accuracy of vehicle target identification in SAR images. Aiming at the problem of high false alarm probability in the process of vehicle target detection in SAR images, this paper analyzes the phenomenological differences between vehicle targets and background objects on SAR images. The fractal theory is used to realize the quantitative representation of the phenomenological characteristics of the above differences and to distinguish the vehicle objects from the natural features. The basic concept, physical properties, research status and development trend of fractal theory are reviewed in this paper. Combined with the complex and variable backscattering characteristics of the object in the SAR imaging process, the pixel intensity of the vehicle object presents the characteristics of sharp fluctuation and obvious gap size on the SAR image. The limitation of Euclidean geometry in describing the above phenomena is pointed out. Based on the fractal theory, the physical quantities of vehicle target features are summarized, such as fractal dimension feature, extended fractal feature and clearance feature. Then, the paper focuses on how to extract various clearance features of vehicle targets based on fractal theory. Based on the analysis of the existing gap degree features, a double variance gap degree feature is proposed in this paper. This feature defines the box mass as the variance of the pixel amplitude in the box, then calculates the standard variance of the box mass, and obtains the double variance of the pixel set in the whole slice. The comparison and analysis of various measured data show that the characteristics of double variance gap degree can improve the performance of vehicle target identification in SAR images and have good stability. By mining the significant differences between vehicle targets and natural objects on high resolution SAR images, a new feature vector of high dimensional stratified clearance degree is proposed based on fractal theory. The feature vector can quantitatively describe the clearance degree of vehicle target edge contour and the irregularity of target pixels at different scales, and can be used to eliminate false alarm generated by natural objects in the process of target extraction. In the calculation of the feature vector, the linear transformation of the measured slice data is firstly carried out to unify the dynamic range of the gray level of the pixel. Then, the pixel amplitude variance of each layer is taken as the one dimensional component of the feature vector of high dimensional stratification gap degree. Finally, a fuzzy C-means clustering method is used to deal with the feature vectors of high dimensional stratified clearance degree, and the membership function is used to identify the vehicle objects from the background objects. In this paper, simulated SAR image data, MSTAR database and domestic independent airborne SAR image data are used to test the discriminant performance of various gap features extracted in this paper, and to compare and analyze them.
【學位授予單位】：國防科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
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