【作者】 陈琪；

【导师】 匡纲要；

【作者基本信息】 国防科学技术大学 ， 信息与通信工程， 2011， 博士

【摘要】 本文研究了合成孔径雷达(Synthetic Aperture Radar,SAR)图像的港口提取技术。考虑到港口SAR图像数据量大的特点,以及当前实际应用中的大场景图像处理的时效性需求,本文提出港口提取的分层处理思路,即首先从大场景图像中分离出海域(获取海陆二值图),然后在海陆二值图中检测出包含港口目标的感兴趣区域(RegionofInterest,ROI),进而在港口区域内部进行舰船的检测,最后对检测到的舰船进行鉴别。按照这一思路,在分析港口目标配置及成像特点的基础上,分别对海陆分割、港口检测、港口区域舰船检测及鉴别进行了深入的研究。开展的工作主要包括以下几个方面:(1)港口的目标配置与其SAR图像的特性分析。归纳总结了港口的一般配置,建立了港口的一般模型,分析了港口的主要散射机制及港口在SAR图像中的影像特点。这些工作为后续研究的开展奠定了基础。(2)SAR图像海陆分割。为提供海陆二值图给后续港口检测任务,以准确、高效地完成海陆分割为目标,提出了一种改进的二维OTSU分割方法。首先,二维直方图比一维直方图更容易区分目标与背景;其次,通过分析传统二维OTSU法中关于二维直方图主对角区域概率假设的缺陷,修正主对角区域概率计算,大大提高了分割精度;再次,在理论分析其计算量的基础上,推导出了相应的快速算法,提高了算法的实用性,能够达到为后续港口检测提供海陆二值图的目的。(3)SAR图像港口检测。为提供港口ROI给后续港口区域舰船检测任务,以正确检测到港口且准确定位其边界轮廓为目标,提出了一种基于特征的粗精两级港口检测框架。同时利用港口突堤分布特征和岸线封闭性特征,二者的结合有效克服了在港口突堤分布相对松散、岸线形状复杂等情况下港口检测效果不佳的问题。新方法通过建立港口特征模型,重点解决了突堤合并中目标完整性要求及虚警的滤除、突堤特征点选择、特征点岸线封闭性计算等问题。实验结果证明新方法具有检测性能高、定位准确等优点。(4)SAR图像港口区域舰船检测。为提供舰船ROI给后续港口区域舰船鉴别任务,以高检测性能为目标,提出了一种港口区域舰船检测新方法。首先,根据高精度港口检测结果得到扩展区域,将获取的港口扩展区域灰度图作为待检测图;其次,提出了一种基于G0分布的港口内舰船CFAR检测算法。该方法能够在一个广泛的均匀度变化范围内对杂波图像进行较为精确建模,通过有效杂波自动筛选的引入,使得该检测算法具有恒虚警率特性并能够取得较好的检测性能。(5)SAR图像港口区域舰船鉴别。提出了一种港口区域舰船鉴别新方法。首先,针对舰船目标的特殊性,提出了一种新的形状特征;其次,利用冗余性、鲁棒性和可分离程度度量定量分析了目标切片常用的鉴别特征,给出了适合舰船目标的优选鉴别特征序列及其优选方法;再次,给出了加权最小距离分类器的设计,该分类器根据使优选鉴别特征矢量具有最大可分性对应的权重,修正已有分类器。实验结果证明新方法具有鉴别精度高、速度快等优点。更多还原

【Abstract】 With the demand on harbor interpretation with SAR image, the techniques ofextracting harbor from SAR images are studied in this thesis. In order to deal withlarge-scene images in practical applications, this paper proposes a hierarchicalprocedure for harbor extraction. Firstly, the sea area is separated from the large-sceneimage (sea-land binary image extraction); secondly, harbor detection is implementedfrom the sea-land binary image (ROI extraction); then, the ship detection is realizedinsidethedetectedharbor;finally,theshipdiscriminationisimplementedforshipROIs.According to this procedure, this paper has a detailed research on the techniques ofsea-land segmentation, harbor extraction, ship detection and discrimination inside theharborarea.Themainworkofthisthesis includesthefollowingaspects.(1) The disposal of the harbor and the characteristics of SAR images are firstlyanalyzed. The general disposal ofthe harbor is concluded and the general harbor modelis established. Then the main scattering mechanisms encountered in harbor areas andtheir characteristics in SAR images are analyzed. The above analysis on the harbor isthefoundationofthe subsequentresearch.(2)Accordingtotherequirementofprovidingthe sea-landbinaryimageforharbordetection, an accurate and efficient method to segment the sea areas from SAR imagesis proposed. Firstly, the objects and the background are easier to distinguish in a 2Dhistogram than that in a 1D histogram. Secondly, the assumption about themain-diagonal probabilities is unreasonable, which is used with the 2D histogram in atraditional 2D OTSU method. We corrected the calculation of the probabilities at themain-diagonal region,andthesegmentationprecisionis greatlyimproved. Accordingtothe theoretical analysis, a fast recursive method for realizing modified 2D OTSU isobtained, which makes the sea-land segmentation algorithm more practical. Moreover,proposed method can meet the practical application demands of providing sea-landbinaryimageryforharbordetection.(3) Since the existing methods of harbor detection from SAR images are notapplicable for images with different types of harbors, this paper proposes a method ofhabor detection based on features, in order to detect harbor and acquire correspondingboundaries accurately. This algorithm not only makes use of the characteristics ofharborjetty, which has longstrip in shape andconcentrative space distribution, but alsoutilizes the characteristics of the closed harbor coastline, which is surrounded by theland. The combination of the characteristics of harbor jetty and harbor coastline canovercome the problems, that the performance of harbor detection is worse when harborjetties has comparatively incompact distribution and the shape of coastline iscomplicated.Theexperimentalresultsshowthatthenewmethodiseffectivewithahigh detection rate, a low false-alarm rate and good localization performance. The detectionresults can meet the demands of providing harbor ROI for ship detection inside harborregion.(4) According to the requirement of providing ship ROIs for ship discrimination,an effective and efficient method of detecting the ships inside the harbor region fromSAR images using a CFAR detector based on the G0distribution is proposed. Firstly,theSAR imageoftheharborcoastwise regionisextractedbasedontheharborcoastline.Then, a detailed analysis is presented on the clutter statistical properties of harborcoastwise region in SAR image. Further, the ship detection is completed based on theCFAR detector with the G0 distribution. The proposed method can precisely modelclutter data under different clutter environment statistically. By introducing theautomatic censoring of effective clutter pixels, our method has a constant false alarmrate and good performance of detection. The detection results can meet the applicationdemandsofprovidingshipROIsforshipdiscrimination.(5) Aiming at higher precision for discrimination, a method of ship discriminationbased on feature extraction and selection is proposed. Firstly, a new shape feature isconstructed for ship discrimination. Then, the common features for discrmination arequantitatively analyzed by the redundancy, robustness and separability of features. Amethod of selecting the optimal features for target discrimination is given. Finally, aweighted minimum distance classifier is designed to improve the performance of theexistingclassifiers.Theexperimental results show that thenewmethodiseffectivewithhighclassificationaccuracy andexcellentdicriminationperformance.更多还原