面向舰船目标检测的单通道复值SAR图像统计建模方法研究

合成孔径雷达(SAR)成像模式丰富、覆盖范围广、分辨率高，可以长期、动态、宏观地对海洋进行监测。在完全发展的相干斑假设条件下，传统单通道SAR图像舰船目标检测方法主要研究幅度信息。然而，其部分假设条件在高分辨率情形下并非严格成立，因此无法有效利用单通道SAR图像的相位或复值信息。该文面向舰船目标检测应用，将单通道复值SAR图像统计建模方法划分为幅度、相位和复值统计建模3个部分，首先简要综述了单通道SAR图像幅度统计建模方法，然后详细阐述了单通道SAR图像相位和复值统计建模方法，并重点介绍了其建模过程和参数估计方法。在此基础上，该文给出了作者研究小组在基于复值统计信息的单通道SAR图像舰船目标检测方面的部分最新研究结果，并分析展望了下一步研究方向。      

Sparse representation-based algorithm for joint SAR image formation and autofocus

Inaccuracies in the observation model of the synthetic aperture radar (SAR) due to inaccuracies of the velocity and position of the platform or atmospheric turbulence cause degradations in reconstructed images which necessitate the use of autofocus algorithms. In this paper we propose a novel signal processing algorithm for joint SAR image formation and autofocus in a synthesis dictionary based sparse representation framework. Proposed algorithm can be applied broadly to scenes that exhibit sparsity with respect to any dictionary. This is done by extending our previously developed sparse representation-based SAR imaging framework to joint SAR image formation and autofocus. To this end, the phase error vector is separated from the unknown phase of the complex-valued back-scattered field. Phase error vector is estimated using a MAP estimator and compensated through an iterative algorithm to produce focused images. We demonstrate the effectiveness of the proposed approach on synthetic and real imagery.     

基于自适应加权方法的复值SAR图像的频域压缩改进算法

该文提出了一种基于自适应加权方法的复值SAR图像的频域压缩改进算法。该算法在原有频域压缩算法的基础上采用了自适应的加权方法,在低压缩比(20:1)的情况下能够很好地保留原始数据图像的特征,是一种有效的复值SAR图像的压缩算法。复值空域相关系数指标评估,以及原始数据、重建数据的幅度图对比和两幅数据图像之间的相关系数图都验证了算法的有效性。     

Compression of complex-valued SAR images

Synthetic aperture radars (SAR) are coherent imaging systems that produce complex-valued images of the ground. Because modern systems can generate large amounts of data, there is substantial interest in applying image compression techniques to these products. We examine the properties of complex-valued SAR images relevant to the task of data compression. We advocate the use of transform-based compression methods but employ radically different quantization strategies than those commonly used for incoherent optical images. The theory, methodology, and examples are presented.     

A novel lacunarity estimation method applied to SAR image segmentation

Based on the relative differential box-counting algorithm and the gliding-box algorithm, a novel method for estimating the lacunarity features of grayscale digital images is proposed. Four natural texture images are used to test the performance of the novel lacunarity measure. Comparisons with published methods show that the proposed method can efficiently describe texture images, and provide accurate classification results. Real synthetic aperture radar (SAR) images analyses are found to have different lacunarity values for different regions. We show that good results can be obtained with appropriate lacunarity parameters applied to SAR images segmentation.     

A hybrid feature extraction method for SAR image registration

Extracting and matching correct correspondence between two images are significant stages for feature-based synthetic aperture radar (SAR) image registration. Two methods of feature extraction were employed in this study. Blob features were obtained by combining a Gaussian-guided filter (GGF) with a scale invariant feature transform, and corner features were obtained from the GGF. A GGF can store edge information and operate more effectively than a Gaussian filter. The ratio of average was used to compute gradients in order to reduce the speckle effect. Fast sample consensus (FSC) algorithm was combined with complete graph method for feature correspondence matching. Although FSC algorithm can extract valid correspondence, it may not be efficient enough to deal with SAR images due to its random nature and the large number of outliers in the data. Therefore, a graph-based algorithm was employed to solve the problem by eliminating outliers. The proposed hybrid method was tested on several real SAR images having different properties. The results showed that the proposed method performed the automated registration of SAR images more accurately and efficiently.     

Watershed identification of polygonal patterns in noisy SAR images

The paper describes a new approach to pattern recognition in synthetic aperture radar (SAR) images. A visual analysis of the images provided by NASA's Magellan mission to Venus has revealed a number of zones showing polygonal-shaped faults on the surface of the planet. The goal of the paper is to provide a method to automate the identification of such zones. The high level of noise in SAR images and its multiplicative nature make automated image analysis difficult and conventional edge detectors, like those based on gradient images, inefficient. We present a scheme based on an improved watershed algorithm and a two-scale analysis. The method extracts potential edges in the SAR image, analyzes the patterns obtained, and decides whether or not the image contains a "polygon area". This scheme can also be applied to other SAR or visual images, for instance in observation of Mars and Jupiter's satellite Europa.     

基于ROI和证据理论的目标融合检测算法

考虑合成孔径雷达图像(SAR)和光学图像的互补特性,提出了一种基于感兴趣目标区域(regions of interest, ROI)决策层融合的军事目标检测方法:分别在SAR图像和光学图像中提取出ROI,再利用各自的统计特征和几何特征给提取出的ROI分配置信度,以表示正确鉴别ROI的概率。最后在决策层上运用D-S证据理论融合两个源中同一ROI的置信度,获得更可靠的融合检测结果。该方法很好的实现了SAR和光学图像的优势互补,并在对遥感图像测试集的试验中得到了验证。      

用小波变换抑制SAR图像中的斑点噪声

抑制合成孔径雷达图像中的斑点噪声一直是处理图像并得到准确图像信息的难点，提出了一种基于小波变换抑制合成孔径雷达（SAR）图像中的斑点噪声的方法，对原有的小波变换方法作了改进，能更好地保留图像的边缘信息，并能简化计算量。在仿真实验中使用了合成的模拟图像和真实的合成孔径雷图像，并与以往的小波去噪滤波方法以及一些经典的斑点噪声滤波方法（包括中值滤波，Lee滤波，Frost滤波）进行比较，在综合考虑了滤波算法在均匀区域对斑点噪声的抑制能力以及保留边缘信息能力的情况下，提出的算法有更好的效果。     

SAR图像的自动分割方法研究

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,该文提出了一种SAR图像的自动分割方法。首先在特征提取阶段,通过计算小波能量提取纹理信息,用邻域统计量提取灰度信息,用保边缘平均灰度提取边缘信息,以确保边缘准确。然后提出一种改进的完全无监督的聚类算法进行图像分割,该算法可以自动确定分割的类型数目。由于该方法充分考虑了SAR图像的纹理、灰度和边缘信息,因而极大地提高了其最终分割性能。实验结果证明了该方法的有效性。