近场高分辨SAR图像增强方法研究

发布时间：2018-12-12 21:44

【摘要】：合成孔径雷达(SAR)是一种具有高分辨成像特性的雷达,在各个领域都有着极为重要和广泛的应用。近年来,近场SAR高分辨成像逐渐成为了研究的热门,在医疗成像,生物科学,安检防恐等多个方面都有很广阔的应用前景。但是目前近场高分辨SAR图像自身存在许多问题影响了图像的质量,制约其后续应用。其主要问题就是近场SAR图像的高旁瓣和低对比度。高旁瓣不仅影响目标点的识别还会由于重叠而形成虚假目标。低对比度影响着图像的视觉效果,使得图像模糊,论文围绕近场高分辨SAR图像增强问题重点研究了近场SAR图像的旁瓣抑制技术和对比度增强技术,其主要研究内容如下:论文首先从成像角度分析近场SAR图像的特点。说明了高旁瓣和低对比度是近场高分辨SAR图像最为主要的两个问题。针对图像的旁瓣抑制提出了分辨率,峰值旁瓣比和积分旁瓣比三个旁瓣抑制评估指标。针对图像的对比度增强提出了图像熵和增强度准则两个指标。这些指标为图像旁瓣抑制和对比度增强的研究提供了客观的评估。针对近场SAR图像旁瓣抑制技术进行了细致的研究。首先阐述了旁瓣产生的机理,随后介绍了常用的抑制旁瓣的窗函数,并指出这些窗函数的不足之处。然后研究了旁瓣走向和图像频谱之间的关系,引出频谱变形抑制旁瓣方法,针对图像旁瓣抑制后主瓣展宽的现象对该方法进行了改进,提出了频谱加权旁瓣抑制方法并通过理论推导说明了该方法的可行性。该方法在有效抑制旁瓣的同时又严格控制主瓣的展宽,通过多组数据的实验与对比以及旁瓣评估指标的说明,验证了该方法的有效性。针对近场SAR图像对比度不强的问题进行研究。介绍了常用的图像的对比度增强方法,指出在近场SAR图像中这些方法的不足。随后引入基于图像二维最大熵和最大方差的图像分割方法。在图像分割的基础上对近场SAR图像进行图像增强,克服了近场SAR图像背景目标干扰较为严重的问题,使得图像对比度增强达到不错的效果。使用多组实验数据进行对比和评估指标的评估,都证明了在图像分割的基础上,近场SAR图像增强可以取得更好的效果。
[Abstract]:Synthetic Aperture Radar (SAR) is a kind of radar with high resolution imaging, which has been widely used in many fields. In recent years, near-field SAR high-resolution imaging has gradually become a hot research, in medical imaging, biological science, security prevention and other aspects of a wide range of applications. However, there are many problems in the near field high resolution SAR image itself which affect the quality of the image and restrict its subsequent application. The main problem is the high sidelobe and low contrast of near field SAR images. High sidelobe not only affects the recognition of target points, but also results in false targets due to overlap. Low contrast affects the visual effect and blurs the image. This paper focuses on the sidelobe suppression and contrast enhancement of near field SAR images around the near field high resolution SAR image enhancement problem. The main research contents are as follows: firstly, the characteristics of near field SAR images are analyzed from the point of view of imaging. It is shown that high sidelobe and low contrast are the two most important problems in near field high resolution SAR images. Three evaluation indexes of sidelobe suppression are proposed for image sidelobe suppression such as resolution peak sidelobe ratio and integral sidelobe ratio. Image entropy and enhancement criterion are proposed for image contrast enhancement. These indexes provide an objective evaluation for the study of sidelobe suppression and contrast enhancement. The sidelobe suppression technique of near field SAR images is studied in detail. In this paper, the mechanism of sidelobe is introduced, and then the common window functions which suppress sidelobe are introduced, and the shortcomings of these window functions are pointed out. Then, the relationship between sidelobe orientation and image spectrum is studied, and the method of spectrum distortion suppression sidelobe is introduced. The method is improved for the phenomenon of main lobe broadening after sidelobe suppression. A spectrum weighted sidelobe suppression method is proposed and its feasibility is demonstrated by theoretical derivation. The method can effectively suppress the sidelobe and strictly control the broadening of the main lobe. The validity of the method is verified by the experiments and comparisons of multiple sets of data and the evaluation index of the sidelobe. The problem of low contrast of near field SAR images is studied. This paper introduces the contrast enhancement methods of common images, and points out the shortcomings of these methods in near field SAR images. Then an image segmentation method based on two-dimensional maximum entropy and maximum variance is introduced. On the basis of image segmentation, the near field SAR image is enhanced, which overcomes the problem of serious interference of background target in near field SAR image, and makes the contrast enhancement of the image achieve a good effect. It is proved that the near field SAR image enhancement can achieve better results on the basis of image segmentation by comparing and evaluating the evaluation indexes with multiple sets of experimental data.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.52

【相似文献】