【摘要】 在合成孔径雷达(SAR)图象中,需要对其中的人工和自然目标进行识别和分类,目标的特征提取是自动目标识别系统中的关键部分.一般图象的特征会采用颜色、纹理、形状等特征量.在SAR图象中,由于存在大量的背景噪声、目标边界模糊等问题,造成以上特征量在分类时往往不能达到很好的分类效果.作者提出了一种典型目标特征提取和分类识别方法.这种方法基于SAR成像的原理,在图象灰度阈值分割后做粗搜索分别提取灰度特征,椭圆矢量特征,以及栅格特征,然后进行SAR图象典型目标的分类识别.实验表明,这种方法效果较好.更多还原

【Abstract】 In synthetic aperture radar(SAR) imaging,man-made and natural object recognition and classification is necessary.Object’s feature extraction is a key component in automatic target recognition.In most cases,color,texture and shape are adopted as image’s features which are not functioned well in SAR images due to a lot of noise in the background and blurry boundary. Authors propose a method for object feature extraction and recognition of SAR images.In the method,based on the theory of SAR imaging,the image intensities are segmented and used as the masks of searching regions.Then,the image is searched sparsely and gray feature,ellipse vector feature,and grid feature are extracted.Finally,the typical objects in the image are classified and recognized.Experiments show that the method is preferable efficient and satisfactory.更多还原