共查询到19条相似文献,搜索用时 156 毫秒
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极化合成孔径雷达(synthetic aperture radar,SAR)图像为雷达图像中的信息处理和获取提供了更为便捷的途径。提出了基于独立分量分析(independent component analysis,ICA)的极化SAR图像相干斑抑制方法。该方法将极化SAR图像斑点噪声的乘积模型,变换为应用ICA的信号加噪模型。并且将HV/VV的比值图像,也作为ICA的输入数据。分别使用几种不同的ICA算法,得到了分别对应于HH、HV和VV极化的3幅降噪图像,并对结果进行了比较分析。实验结果表明,应用ICA算法可以有效地降低极化SAR图像的相干斑噪声,提高图像质量。 相似文献
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一种改进的自适应窗口滤波的SAR图像去噪算法 总被引:1,自引:0,他引:1
SAR的广泛应用及其距离、方位上的高分辨率,使其受到人们越来越多的重视。但是SAR图像中的斑点噪声却严重地制约了它的进一步的应用。根据SAR图像的成像过程提出了一种改进的抑制SAR图像中斑点噪声的算法-自适应窗口空间域平滑算法。它根据窗内像素点的统计特征自适应调节窗口大小,并通过门限来决定是否需要改变窗的尺寸,达到在同性质区增大窗口来抑制斑点噪声,同时在异性质区减小窗口以最大限度地保护图像细节的目的。对实际SAR图像的验证,说明它在抑制斑点噪声和保存图像细节信息方面较之原方法有了一定的改进和提高。 相似文献
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采用一种新的基于盲信号分离(BSS)和序列非线性滤波方法实现多极化合成孔径雷达(SAR)影像相干斑噪声抑制和水体目标快速提取。SAR影像具有强烈乘性相干斑噪声,影像数据为非高斯分布,但其具体分布形式及参数难以获得。利用基于独立分量分析的盲信号分离方法,不需要知道SAR影像的具体分布,通过对数量化将相干斑噪声转化为与图像数据相互独立的加性噪声,从多极化SAR影像中自动分离出图像数据与相干斑噪声,并自动选择相干斑指数最小的分量为图像分量。针对SAR影像水体目标的亮度及形状分布特征,进一步采用序列非线性滤波处理,从分离出的图像分量中提取出水体目标。利用ENVISAT ASAR多极化影像进行了实验,结果表明该方法可以快速准确地提取多极化SAR影像中的水体目标。 相似文献
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SAR图像固有的斑点噪声严重影响了图像的判读和后续处理,因此抑制SAR图像斑点噪声显得尤其重要。一个良好的斑点噪声抑制算法应该在降低斑点噪声的同时,能很好地保持图像的细节特征,但现有的去噪算法没有一种能够完美地解决这个问题的。本文基于SAR图像斑点噪声滤波的重要性以及存在的问题,将整体变分偏微分方程用于去除斑点噪声。根据整体偏微分方程建立了去噪模型,并分析了模型的性能和参数选择的重要性。通过实验验证了该算法的有效性,并以峰值信噪比(PSNR)为评价准则,利用多项式拟合方法选择了最优参数。引入边缘保持指数(EPI),与其他滤波算法比较,本算法在去除噪声的同时较好地保持了边缘。 相似文献
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孙增国 《计算机应用与软件》2012,29(7):18-21
合成孔径雷达(SAR)图像由于相干成像方式产生斑点,严重干扰人们对图像的解译。所提系统总结了基于最小均方误差(MMSE)准则的SAR图像降斑算法,包括Lee滤波、Kuan滤波和Frost滤波,分析了它们在斑点抑制和边缘保持两方面的特性。仿真图像和真实SAR图像的降斑实验表明,Frost滤波算法同时考虑滑动窗内的统计特性和结构信息,因此优于其它滤波算法。 相似文献
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为了改善SAR图像的质童,提出了劣质SAR图像的退化模型.根据在斑点非完全发育的非平稳域,SAR图像受斑点噪声污染或线性模糊退化两种主要因素降质的特点,设计了两种具有代表性的复原流程,并采用复小波域层内层间相关性斑点抑制算法和维纳去卷积盲复原算法进行复原.实验结果表明,复原后图像的视觉效果和PSNR值均有明显的改善. 相似文献
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针对合成孔径雷达(SAR)影像相干斑噪声强烈且分布形式及参数获取困难的问题,提出一种基于独立分量分析(ICA)和序列非线性滤波(SNF)实现多极化SAR影像相干斑噪声抑制和机场目标快速提取方法。利用ICA从多极化SAR影像中自动分离出图像数据与相干斑噪声,自动选择相干斑指数最小的分量为图像分量。通过SNF从分离出的图像分量中提取出机场目标。采用ENVISAT ASAR多极化影像进行实验,结果表明该方法能快速准确地提取多极化SAR影像中的机场目标。 相似文献
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针对现有相干斑抑制算法不能在去除斑点噪声和保持图像边缘、细节信息之间做到很好的折中,提出了一种新的基于形态Haar小波变换的合成孔径雷达(SAR)图像斑点噪声抑制方法。该方法首先对SAR图像进行二维形态Haar小波分解,图像的边缘、细节和纹理信息在低频子带中得到了更好的保留,噪声主要分布在高频子带;然后,根据各高频子带噪声的特点,分别对高频子带进行均值和中值滤波达到去除斑点噪声的目的;最后,再对低频子带和处理后的高频子带进行形态Haar小波精确重构得到去斑图像。实验证明:该算法不仅大大改善了原始SAR图像的画面质量,同时很好地保持了原始SAR图像的纹理特性和细节信息;该算法去斑性能指标总体优于传统的Lee滤波、Frost滤波、Kuan滤波和小波软阈值法。 相似文献
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L. ALPARONE S. BARONTI R. CARLAgrave C. PUGLISI 《International journal of remote sensing》2013,34(7):1357-1365
An algorithm based on local order statistics is proposed for adaptive reduction of speckle noise in synthetic aperture radar (SAR) images. A selective smoothing is obtained by replacing a pixel value belonging to either of the tails of the local histogram by its percentile, whose area is adaptively defined by a Gaussian function of the Local Variation Coefficient. The filter can fit the actual noise level and preserves structures, textures, and point targets, as well as the local mean without introducing any blur on the edges, mostly due to its closure property. Comparisons with algorithms suitable for speckle smoothing are performed on true SAR images and show selective signal-to-noise ratio (SNR) enhancements. 相似文献
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相干斑噪声是SAR图像的固有特点。对相干斑抑制的要求是在平滑噪声的同时,尽量保持原始图像的结构信息。现有的许多相干斑抑制方法各有优点和不足,没有普遍的适用性。基于图像在小波域的隐马尔可夫模型(HMMs)结构,结合SAR图像中相干斑噪声的统计特性,本文提出了一种新的小波域相干斑抑制方法。仿真及实测数据处理结果表明,该方法在有效抑制相干斑的同时,更好地保持了边缘结构。与小波域软阈值去噪方法和Lee滤波器相比较,该方法在噪声平滑及边缘保持上都取得了较大的改进,并得到了较好的视觉效果。 相似文献
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目的 相干斑的存在严重影响了极化合成孔径雷达(PolSAR)的影像质量.对相干斑的抑制是使用SAR数据的必不可少的预处理程序.提出一种基于非局部加权的线性最小均方误差(LMMSE)滤波器的极化SAR滤波的方法.方法 该方法的主要过程是利用非局部均值的理论来获取LMMSE估计器中像素样本的权重.同时,在样本像素的选取过程中,利用待处理像素的极化散射特性和邻域块的异质性来排除不相似像素以加速算法,同时达到保持点目标和自适应调节块窗口大小的目的.结果 模拟影像和真实影像上进行的实验结果表明,采用这种方法滤波后影像的质量得到明显改善.和传统的LMMSE算法相比,无论是单视的影像还是多视的影像,本文方法去噪结果的等效视数都高出8视以上;峰值信噪比也提升了5.8 dB.同时,去噪后影像分类的总体精度也达到了83%以上,该方法的运行效率也比非局部均值算法有了较大提升.结论 本文方法不仅能够有效抑制相干斑噪声,还能较好地保持边缘和细节信息以及极化散射特性.这将会为后续高效利用SAR数据提供保障. 相似文献
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目的 相干斑噪声严重影响SAR影像解译。抑制相干斑同时,获取较好的边缘保持效果始终是相干斑抑制的重点。针对该问题,提出一种引入欧氏距离的各向异性扩散(EDAD)相干斑抑制方法。方法 EDAD算法以P-M模型与SRAD算法为基础,利用邻近像素间区域欧氏距离代替原有边缘检测算子,自适应区分同质区与异质区,有效构造各向异性扩散系数,完成相干斑抑制。结果 运用EDAD算法与现存各向异性扩散算法对截取的两景TanDEM-X影像进行试验研究并比较各类算法的评估参数。EDAD算法的等效视数分别为3.996与5.859,均高于其他算法,体现优越的相干斑抑制能力;EDAD算法相干斑抑制前后比值影像的均值分别为0.999与1.001,方差分别为0.270与0.269,较其他算法均更接近理想值1与0.273,展现更优边缘保持与相干斑抑制能力。结论 本文算法可有效提高边缘检测能力,获取更优相干斑抑制效果。经验证,对分布较散的弱相干斑区域与分布较集中的强相干斑区域均有较好适用性。 相似文献
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Swapan Chakrabarti Colin Axel Prasad Gogineni 《International journal of remote sensing》2013,34(5):1804-1828
Synthetic aperture radar (SAR) is used extensively for remote-sensing applications due to its ability to operate under all weather conditions and provide high-resolution images. However, high-resolution images constructed from SAR data often suffer from speckle, which makes identification and classification of edges/boundaries a difficult task. Speckle noise is multiplicative in nature and is a result of constructive and destructive interference of signals from randomly distributed scatterers in a resolution cell illuminated by a coherent signal. Usually, speckle is reduced by incoherent averaging of high-resolution image pixels that degrade resolution. The principal goal in all speckle-reduction algorithms is to reduce speckle with minimum loss of resolution. In this investigation, we used specially trained and validated artificial neural networks (ANNs) for speckle reduction in images generated with a radar-depth sounder/imager and compared their performance to the conventional adaptive filtering and Speckle Reducing Anisotropic Diffusion (SRAD) algorithm. We show that by training different ANNs to reduce speckle noise at different levels of signal-to-noise ratio (SNR), rather than training one ANN to operate at all levels of SNR, improved performance in speckle reduction can be obtained. Real SAR images and synthetic noise are used in this research to compare the performance of the proposed ANN-based approaches with that obtained from conventional methods. This investigation shows that on combining the results from a set of properly trained and validated neural networks, the SNRs of the output images improve beyond those obtained from conventional approaches when the input SNRs are greater than or equal to 4 dB. For input SNRs greater than 0 dB, however, the ANNs provide better performance in edge preservation compared with conventional methods. We also found that once a set of ANNs is properly trained to reduce speckle from an image, these ANNs can be used in de-speckling other images without any further training. The merits and demerits of different configurations of the ANNs are studied to find useful speckle noise-tolerant ANN architectures. 相似文献
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A filter for suppressing speckle in synthetic aperture radar (SAR) images utilizing wavelet is proposed. The filter suppresses speckle by reducing the amplitude of the detail images in wavelet subspaces, while preserving edges by releasing the amplitude reduction around edges; information on edges, contained in the detail images, is utilized for edge detection. Simulations and application to SAR images have shown that the performance of the filter is satisfactory in both smoothing and edge preservation, and in generating visually-natural images as well. 相似文献
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Shi-qi Huang Author Vitae Dai-zhi Liu Author VitaeAuthor Vitae Xi-jian Guo Author Vitae 《Pattern recognition》2009,42(7):1533-553
It is very difficult to detect small targets when the scattering intensity of background clutter is as strong as the targets and the speckle noise is serious in synthetic aperture radar (SAR) images. Because the scattering of man-made objects lasts for a longer time than that of background clutter in azimuth matching scope, it is much easier for man-made objects to produce strong coherence than ground objects. As the essence of SAR imaging is coherent imaging, the contrast between targets and background clutter can be enhanced via coherent processing of SAR images. This paper proposes a novel method to reduce speckle noise for SAR images and to improve the detected ratio for SAR ship targets from the SAR imaging mechanism. This new method includes the coherence reduction speckle noise (CRSN) algorithm and the coherence constant false-alarm ratio (CCFAR) detection algorithm. Real SAR image data is used to test the presented algorithms and the experimental results verify that they are feasible and effective. 相似文献