【作者】 罗峰；

【导师】 滕奇志；

【作者基本信息】 四川大学 ， 模式识别与智能系统， 2005， 硕士

【摘要】 合成孔径雷达(SAR)能够提供全天候条件下高分辨率的详细的地面测绘资料和图像,这种能力对于现代侦察任务是至关重要的。由于这些优点,使得SAR广泛的应用于地质勘探,地面监测,军事侦察等领域。也使SAR图像的处理和研究成为当前信号处理领域的一个热点。 本文主要研究了SAR图像的自动目标识别。在SAR图像目标识别领域里,主要有模板匹配、模式识别以及近年来发展的基于模型的目标识别技术。模板匹配使用样本数据作为模板,计算相关系数得到识别结果;后两种技术通过对样本进行特征提取并建立特征库,以此特征来匹配待搜索图像以达到识别目的。模板匹配要求待匹配图像像素之间有对应关系,在边缘模糊的SAR图像中,容易造成误匹配。我们提出了栅格矢量法,克服了边缘模糊导致的匹配困难。 文章分析和讨论了几种常见的特征提取方法,基于纹理特征的灰度共生矩阵,基于形状的不变矩特征,但其应用于SAR图像效果不理想。我们提出了基于目标边缘的Hausdorff距离图像作为目标特征量,这一特征可以克服平移,旋转,小的放缩变换,是一种合适的SAR图像特征。 本文建立了一种有效的自动目标识别系统,系统为检测器,鉴别器,分类的三级结构。检测器使用了双参数CFAR算法,粗选出兴趣区域(ROI),结合SAR图像特点,提出使用阈值分割的方法选取潜在目标区域,使用滤波法去除更多还原

【Abstract】 Synthetic Aperture Radar(SAR) can provide all-weather terms high resolution topography imagery in detail, and also capable of penetrating through observation view port ,which is significant to reconnaissance task. Because of these advantages, SAR is widely applied in many fields such as geological exploration, ground and military surveillance, etc. And also make SAR image processing and research become a hot topic in signal processing domain .This paper mainly focused on Automatic Target Recognition (ATR ) in SAR imagery. In the field’ s of SAR ATR, the leading techniques include template matching, pattern recognition and model based technique which is developed recent years. The template matching use sample as the template, calculating cross-correlation coefficient and get the recognition result. The last two techniques build feature library through extracting features from samples, and use these features to match the sensed image and achieve the goal of classification. Template matching needs corresponding relationship between pixels in the sensed image and the pattern, which makes false matching in the blurred image.Several feature extraction methods used frequently are discussed in the paper, co-occurrence matrix based on texture feature and the invariant moment feature based on shape are analyzed in detail, thesefeatures are not appropriate while concerning SAR imagery. We suggestedr Hausdorff Distance(HD) image as the feature which based onedge-extraction, HD is capable of overcoming translation, rotation and scaling, which is a reasonable feature in SAR imagery recognition.In this paper, a three-stage ATR system is introduced; the system is composed of prescreener, discriminator and classifier. The prescreener uses double parameters-CFAR algorithm to roughly detect potential targets. Canny operator is applied in discriminator to extract edges of target, and we use Hausdorff distance transform to get the distance image, after the processing, the correlation is improved remarkably. But because edge detection and distance transform increased the computational cost, during the course of matching in the third step, we suggest two kinds of optimized search strategies to speed up recognition.Some new methods are suggested in this paper: 1)A self-adaptive region segmentation method which based on the difference of mean intensity and deviations, this method avoids threshold selection by operator;2)Grid feature vector is introduced to reduce the size of intensity feature and speed up pattern matching;3)Canny operator is applied to extract edges of target, scaling and translation transform can be overcome by using the Hausdorff transform distance feature;4)We also suggested a kind of search strategy: multi-scale searching based on wavelet decomposing.更多还原