SAR图像舰船目标检测方法研究

发布时间：2018-07-17 06:40

【摘要】： 合成孔径雷达(SAR)是一种在距离向和方位向上都具有高分辨能力的成像雷达,可以全天候、多极化、多频率、多视角地获取数据,并具有一定的云、雨、烟雾、植被、浅层地表穿透能力。近年来SAR得到了快速发展,已被广泛应用于战场侦察、高空摄像、大地遥感、测绘、资源勘探、灾情预报等领域。舰船检测是世界各海岸带国家的传统任务,在军事、民用部门都有着广泛的应用。我国领海广阔,海洋资源丰富,开展SAR图像舰船检测研究具有重要意义。本文主要围绕SAR图像舰船检测开展研究。 SAR图像目标检测的本质是从环境杂波中检测出目标回波,包括研究目标本身及其存在的环境,因此,本文首先研究了海面杂波的统计模型。总结了已有的几种SAR图像杂波分布统计模型的推导和参数估计,介绍了杂波统计模型的评估准则,给出了相关几种杂波分布的拟合方法,最后通过实验,用真实SAR图像切片进行杂波直方图拟合评估。在杂波统计模型研究的基础上,研究了舰船目标检测这个核心问题。首先分析了影响舰船目标检测的因素,然后对现有的局部双参数CFAR和全局CFAR进行了比较,根据杂波背景的分布不同,改进了局部杂波统计模型自适应CFAR的算法,最后详细介绍了基于二次Gamma核检测算法,在复杂背景杂波的情况下,将其应用于舰船目标检测,并给出了实验结果。根据SAR图像舰船目标特点,研究了舰船目标相关参数的估计问题。比较了不同估计方法的参数估计结果,并给出了实验结果。文章最后对全文进行了总结,并对舰船检测的研究做了进一步的展望。
[Abstract]:Synthetic Aperture Radar (SAR) is an imaging radar with high resolution in both range and azimuth directions. It can obtain data all weather, multi-polarization, multi-frequency, multi-angle, and has a certain amount of cloud, rain, smoke, vegetation. Shallow surface penetration. In recent years, SAR has been rapidly developed, and has been widely used in battlefield reconnaissance, high-altitude camera, remote sensing, mapping, resource exploration, disaster prediction and other fields. Ship detection is a traditional task of all coastal countries in the world. It is widely used in military and civil sectors. China has a vast territorial sea and abundant marine resources, so it is of great significance to study ship detection in SAR images. The essence of target detection in SAR image is to detect the target echo from the environment clutter, including the target itself and its existing environment. In this paper, the statistical model of sea surface clutter is studied. This paper summarizes the derivation and parameter estimation of several existing statistical models of clutter distribution in SAR images, introduces the evaluation criteria of the statistical models of clutter, and gives the fitting methods of several related clutter distributions. The true SAR image slice is used to evaluate the clutter histogram fitting. Based on the statistical model of clutter, the core problem of ship target detection is studied. Firstly, the factors affecting ship target detection are analyzed, then the existing local two-parameter CFAR and the global CFAR are compared. The adaptive CFAR algorithm of the local clutter statistical model is improved according to the different clutter background distribution. Finally, the algorithm based on quadratic Gamma kernel detection is introduced in detail, which is applied to ship target detection in the case of complex background clutter, and the experimental results are given. According to the characteristics of ship targets in SAR images, the estimation of ship target parameters is studied. The parameter estimation results of different estimation methods are compared and the experimental results are given. At the end of the paper, the paper summarizes the whole paper and makes a further prospect on the research of ship detection.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TN957.52

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