基于多尺度随机模型的SAR图像无监督分割

合成孔径雷达（synthetic aperture radar,SAR）是一种基于相干原理的成像系统,在SAR图像中存在严重影响图像质量的斑点噪声,使得SAR图像的可靠分割非常困难。〖BP）〗根据SAR图像的成像机理,利用两种多尺度随机模型,即多尺度自回归（Multiscale Autoregressive,MAR）模型和多尺度自回归滑动平均（Multiscale Aautoregressive Moving Average, MARMA）模型,分别来描述同一场景不同分辨率SAR图像像素间的统计相关性,并构造了相应的多分辨混合算法实现SAR图像的无监督分割。试验结果表明,提出的两种无监督分割方法是可行的,且MARMA模型比MAR模型能够更精确地捕捉SAR图像多尺度序列中不同类型地形的统计信息,使分割质量具有明显的改进。     

一种新的基于多尺度似然比检验的SAR图像分割方法*

提出的图像分割新算法利用当图像分辨率改变时,不同目标斑点模式变化方式的不同以及相邻图像尺度间的Markov性,推导得出了多尺度似然比的表达式;该方法同时考虑了多尺度自回归（MAR）模型产生的残差信息和较粗尺度图像的灰度信息,增强了区分度,分割结果更精确;考虑了被分类像素的邻域特性,使其对噪声不敏感,具有稳健性。实验结果表明分割效果是显著的。     

基于多尺度图像块的SAR图像无监督分割

提出了一种基于多尺度图像块的SAR图像无监督分割方法。在利用高斯混合模型进行图像分割时,大多采用的是基于单个像素的分割方法,这种方法由于未考虑像素周围邻域结点的信息,分割精度往往不高。论文考虑到SAR图像具有很强的斑点噪声,为了更好地抑制斑点噪声对分割结果的影响,在多分辨分析的基础上提出了一种基于多尺度图像块的图像分割新方法。实验表明,这种基于多尺度图像块的分割较在单个像素下多尺度Markov模型的MPM分割好,分割精度有了较大的提高。     

SAR图像无监督分割的空间变化混合MAR模型方法

提出SAR（Synthetic Aperture Radar）图像的空间变化混合多尺度自回归（Spatially Variant Mixture Multiscale Auto Regressive,SVMMAR）模型方法,该模型不仅能刻画SAR图像的空间变化性,而且利用了SAR图像多尺度序列的统计特性;采用的分类器是像素标号的极大似然估计,细化的同时简化了传统Bayes分类器;该模型无需预先抑制斑点噪声,就能获得精确分割结果;并且理沧上证明了在图像粗尺度确定分类个数的合理性,在此基础上提出一种在粗尺度确定分类个数的新方法,大大减少了运算量。     

SAR图像斑点噪声抑制与滤除技术

论述了不同时期各种合成孔径雷达(SAR)图像斑点噪声(speckle)抑制与滤除技术,并且对各种斑点噪声(speckle)抑制与滤除算法的特点进行分析。比较了不同算法的优缺点,提出利用多尺度分析小波分析发展滤除斑点噪声新算法,提高SAR的灰度分辨率和空间分辨率,是很有意义的方向之一。     

基于小波变换的SAR图像斑点噪声降噪研究

SAR图像上周期性出现斑点噪声,影响图像的解译。小波变换具有多分辨分析特点。在分析SAR斑点噪声模型的基础上．利用小波变换方法对SAR图像斑点噪声进行抑制,同时给出噪声去除性能评价。实验结果表明,小波变换方法具有较好的斑点噪声去除性能。     

基于遗传算法的SAR图像多尺度分割

基于最大期望（EM）算法与遗传算法（GA）,提出一种有效的多尺度SAR图像无监督分割方法。该方法首先利用混合多尺度自回归（MMAR）模型描述SAR图像中由于雷达斑点所引起的不同尺度和同一尺度内像素之间的统计相依性; 然后将GA与EM结合给出MMAR模型的参数估计算法。这种算法利用最小描述长度（MDL）准则,能够选择模型的分量数;最后利用Bayes分类器实现图像的分割。该方法集遗传算法和EM算法的优点,对初始值有较少的敏感性,避免局部最优解,提高了分割精度。实验结果表明GA EM方法优于EM算法。     

有效的SAR图像多尺度分类算法

为提高分类精度,提出一种基于最大期望(EM)与遗传(GA)算法的多尺度SAR图像无监督分类方法.利用多尺度自回归(MAR)模型描述SAR图像中不同尺度之间的统计相依性,提取多尺度特征.应用混合模型描述多尺度特征,并将GA算法与EM算法相结合给出混合模型的参数估计算法,利用最小描述长度(MDL)准则选择模型的分量教.最后使用Bayes分类器实现了图像的分类与分割.该方法集EM算法和GA算法结合后的优点,对设定初值有较少的敏感性,因而避免了局部最优解.应用于SAP图像的实验表明,在分割精度上GA-EM方法优于MAR模型的算法.     

基于Curvelet变换抑制SAR图像斑点噪声的方法

提出了一种基于Curvelet变换来抑制合成孔径雷达（SAR）图像中噪声的方法，Curvelet是一种新的多尺度变换理论，具有各向异性的特征，克服了小波在处理大干一维的高维信号时的不足。该文在介绍Curvelet变换理论及其实现的基础上，引出了SAR图像斑点噪声的去除方法，并改进了Curvelet算法，降低了其运算复杂度，讨论了SAR图像噪声方差的估计，最后和其它的SAR图像去噪方法作了对比和分析。     

基于谱聚类与混合模型的SAR图像多尺度分割

针对谱聚类方法应用于合成孔径雷达（SAR）图像分割时Laplace矩阵的特征值和特征向量难以计算的问题,结合SAR图像在多个尺度的统计信息,给出了一个包含顶点凝聚、初始分割和分割细化3个步骤的SAR图像多尺度分割方法。首先,用一个顶点数不断减少的凝聚图序列来逼近从SAR图像得到的图;然后应用谱聚类方法对最粗尺度的凝聚图进行分割得到初始分割结果;最后根据SAR图像的统计性质,利用基于混合模型估计的分类后验概率将初始分割结果逐尺度进行细化得到SAR图像的最终分割。实验结果表明了方法的有效性。     

Study on segmentation and interpretation of multi-look polarimetric SAR images

The segmentation and interpretation of multi-look polarimetric synthetic aperture radar (SAR) images is studied. We first introduce a multi-look polarimetric whitening filter (MPWF) to reduce the speckle in multi-look polarimetric SAR images. Then, by utilizing the wavelet multiresolution approach to extract the texture information in different scales and the Markov random field (MRF) model to characterize the spatial constraints between pixels in each scale level, a multiresolution segmentation algorithm (MSA) to segment the speckle-reduced SAR images is presented. The MSA first segments the image at the lowest resolution level and then proceeds to progressively higher resolutions until individual pixels are well classified. An unsupervised step to estimate both the optimal number of texture classes and their model parameters is also included in the MSA so that the segmentation can be implemented without supervision. Finally, in order to interpret the results of the unsupervised segmentation and to understand the whole polarimetric SAR image, we develop an image interpretation approach which jointly utilizes the scattering mechanism identification and target decomposition approaches. Experimental results with the real-world multi-look polarimetric SAR image demonstrate the effectiveness of the segmentation and interpretation approaches.     

多区域SAR图像分割的改进水平集方法

由于合成孔径雷达（SAR）图像易受相干斑噪声的影响,光学图像的分割方法并不适用于SAR图像,更不能获得精确的分割结果对比,因此,首先基于GA^0统计模型定义能量映射函数以代替像素值进行后续处理,减小相干斑的影响;其次,使用水平集算法对处理后的图像进行分割处理,选用了一种形式更为简单的水平集函数,并可以较容易地推广到多区域SAR图像分割情况。实验结果表明,该方法可以减少相干斑噪声对SAR图像分割过程的不良影响,具有较好的准确性。     

A novel multi-scale level set method for SAR image segmentation based on a statistical model

To overcome the problems of large data volumes and strong speckle noise in synthetic aperture radar (SAR) images, a multi-scale level set approach for SAR image segmentation is proposed in this article. Because the multi-scale analysis of SAR images preserves their highest resolution features while additionally making use of sets of images at lower resolutions to improve specific functions, the proposed method is useful for removing the influence of speckle and, at the same time, preserving important structural information. The Gamma distribution is one of the most commonly used models employed to represent the statistical characteristics of speckle noise in a SAR image and it is introduced to define the energy functional. Moreover, based on the multi-scale level set framework, an improved multi-layer approach is introduced for multi-region segmentation. To obtain a fast and more accurate result, a novel threshold segmentation result is used to represent the initial segmentation curve. The experiments with synthetic and real SAR images demonstrate the effectiveness of the new method.     

一种新的SAR图像无监督分割方法

马尔可夫随机场（MRF）在SAR图像分割中有着广泛的应用。由于合成孔径雷达（SAR）图像本身所固有的相干斑噪声的影响,传统方法很难获得准确的分割,因此提出了一种新的基于MRF（Markov Random Field）融合Gaussian-Hermite矩（GHM）的SAR图像无监督分割算法。利用Gaussian-Hermite矩的不同阶矩作为SAR图像特征得到初始分割;将得到的初始分割结果作为MRF随机场的先验模型,通过引入一个基于两成分权重参数的能量函数,利用最大后验概率（MAP）得到最终的分割结果。通过对合成图像及SAR图像分割实验结果的比较,表明了该方法在误分率、抗噪性以及视觉效果上具有更好的效果。     

基于Log-Gabor滤波器的SAR图像变化检测研究

合成孔径雷达(SAR)图像具有良好的操作性能和较高的分辨率.针对多时相SAR图像,提出了一种多尺度多方向的图像变化检测方法.通过斑点抑制各向异性扩散滤波(SRAD)和离散小波变换(DWT)对SAR图像进行预处理,利用Log-Gabor(LG)滤波器组对处理后的无斑点图像进行多尺度多方向设计,将多个方向的最大幅值相加得到基于特征的尺度表示.通过设计最小重叠的传输函数来扩大覆盖广泛的滤波器带宽,对每个尺度进行多方向处理得到差分图像系数,将各尺度的差分系数序列相加进而计算差分图像.最后,利用真实的武汉市SAR图像数据集验证了该方法的性能.     

基于灰色Tsallis熵的SAR图像快速分割*

针对SAR图像斑点噪声及分割速度慢的问题,提出一种基于灰色理论和Tsallis熵的SAR图像快速分割方法。该方法首先对待分割图像进行小波变换,将表征图像概貌信息的低频部分重构为概貌图像,表征图像细节和边缘的高频部分重构为细节图像,并建立了相应的概貌—细节共生矩阵模型;然后利用灰色理论和Tsallis熵设计了基于该共生矩阵的灰色Tsallis熵模型,用于求解最优分割阈值;同时,为加快阈值搜索速度,引入群体智能中的粒子群优化算法。实验结果显示,新方法在抗噪性、分割速度和灵活性三个方面均有明显提高。     

SAR图像多时相组合滤波方法

提出一种SAR图像多时相组合滤波方法,利用同区域多幅时间序列SAR图像构建最优线性组合,得到权矩阵,通过处理使多幅图像在信息互补过程中达到抑制斑噪的目的。利用3幅不同时相真实SAR图像进行滤波实验分析,并与经典SAR图像滤波方法比较,结果表明,该方法能够保持空间细节信息,且去除斑点噪声效果良好,可以同时应用于同一区域多幅SAR图像的滤波处理。     

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.     

A fast SAR image segmentation method based on improved chicken swarm optimization algorithm

Severe speckle noise existed in synthetic aperture radar (SAR) image presents a challenge to image segmentation. Though some traditional segmentation methods for SAR image have some success, most of them fail to consider segmentation effects and segmentation speed at the same time. In this paper, we propose a novel method of SAR image fast segmentation which is based on an improved chicken swarm optimization algorithm. In this method, the positions of the whole chicken swarm are firstly initialized in a narrowed foraging space. Secondly, the grey entropy model is selected as the fitness function of the improved chicken swarm optimization algorithm. Hence, the optimal threshold value is located gradually and quickly by virtue of the foraging behaviors of chicken swarm with a hierarchal order. Experimental results show that our method is superior to some segmentation methods based on genetic algorithm, artificial fish swarm algorithm in convergence, stability and segmentation effects.