【作者】 黄健；

【导师】 周代英；

【作者基本信息】 电子科技大学 ， 信息与通信工程， 2016， 硕士

【摘要】 合成孔径雷达(Synthetic Aperture Radar,SAR)作为一种全天候的相干成像系统,是一种很重要的遥感信息获取手段,在军事领域和民用领域取得了广泛的应用。SAR的自动目标识别技术(Automatic Target Recognition,ATR)不需要人为干涉,计算机就能够对SAR图像自动进行分类识别,受到越来越多的国家的关注和研究。本文研究基于子空间法的SAR目标识别方法,主要研究内容如下:1、由于SAR图像生成原理跟普通的图像不同,使得原始的SAR图像混杂着很多的相干斑噪声,并且阴影、背景和目标混杂在一起,因此有必要在特征提取前进行一些预处理操作。通过对数变换把图像中的乘性噪声转化为加性噪声;滤波方法选择基于小波变换的滤波器,实验结果表明小波变换滤波能够在最大程度的抑制噪声的同时保持目标图像的边缘细节信息;接着采用幂变换操作压缩图像的对比范围,加大目标鉴别能力;采用双参数恒虚警率对图像进行分割;后续进行统一分辨率和能量归一化操作,进一步突出了目标的可识别特征。2、针对线性鉴别分析(Linear Discriminant Analysis,LDA)和二维线性鉴别分析(Two-Dimensional Linear Discriminant Analysis,2DLDA)在面对多类别分类问题时造成的“次优性”问题,研究了基于加权2DLDA方法。基于加权2DLDA方法对不利于分类的边缘类和野值点给予较小的权值,所得到的投影更偏重难以区分的类别,解决“次优性”问题,识别性能优于2DLDA。在此基础上提出了一种结合二维主分量分析(Two-Dimensional Principal Component Analysis,2DPCA)和加权2DLDA的特征提取方法,该方法首先利用2DPCA对SAR图像降维,然后通过加权2DLDA减小类内差异,增大类间差异,两者优势互补,从而提高识别性能。3、本文提出了一个2DPCA和二维局部保持投影(Two-Dimensional Locality Preserving Projection,2DLPP)相结合的SAR图像识别方法。该方法充分结合了2DPCA和2DLPP各自的优点,首先利用2DPCA获得目标的全局结构信息,然后通过2DLPP保护目标的局部结构特征,改善目标识别性能。更多还原

【Abstract】 As an all-time coherent imaging system, Synthetic Aperture Radar(SAR) is an important method of acquiring remote sensing information. SAR has been widely used in military and civilian field. Automatic Target Recognition(ATR) technology of SAR needs no human interference, which means computers alone can complete classification and recognization of SAR imags automatically. Consequently, more and more countries have paid their attention to the research of SAR ATR. This thesis focuses on subspace-based feature extraction methods of SAR images.The main contents are as follows:1. The SAR imaging is quite different from some other imaging techniques, and the original images of that are always mixed with a lot of correlated speckle noise, shadows, targets and backgrounds. So it is necessary to preprocess the images before feature detection. In order to make the target more identified, firstly changing the multiplicative noise into additive noise by logarithmic transformation. Secondly, selecting the filter based on the wavelet transform to filter noise, because experimental results show that this wave filter can suppress speckle noises as much as possible and the edge details can be retained at the same time. And then, compressing the contrast range of image to improve the identification ability of target by power transformation. After that, Segmenting image by using two-parameter constant false alarm rate(CFAR). What’s more, Resolution unification and energy normalization are all taken in the subsequent operations.2. When been used to solve multiclass classification problem, Linear Discriminant Analysis(LDA) and Two-Dimensional Linear Discriminant Analysis(2DLDA) will cause sub optimality problem which can adversely affect the recognition performance, so we studied the weighted 2DLDA. The weighted 2DLDA gives smaller weights to those edge classes and wild points which are unconducive to classification. Then the projection obtained by this way will be easier to distinguish the categories that were not well differentiated before. Therefore the final recognition performance is better than 2DLDA. Based on this, a new method that combines 2DPCA and weighted 2DLDA is proposed. This new method first use 2DPCA to reduce dimensions, then use weighted 2DLDA to decrease the discrepancy within classes and increase the discrepancy between classes. It has a better performance than other methods by complementing each other’s advantages.3. This thesis proposes a new feature extraction method that combing 2DPCA and 2DLPP together. This proposed method takes both advantages of 2DPCA and 2DLPP. Firstly, this method makes use of 2DPCA to acquire the global structure information of the target, and then it maintains the local structure by 2DLPP. Performance of targets identification would be improved by the procedures above.更多还原