基于上下文分析的无监督分层迭代算法用于SAR图像分割

基于聚类的分割算法能够有效地分析目标特征在特征域的分布结构,进而准确判断目标的所属类别,但难以利用图像的空间和边缘信息,而基于区域增长的分割算法能够在空间域利用多种图像信息计算目标之间的相似性,但缺乏对特征结构本身的深层挖掘,容易出现欠分割或过分割的结果. 本文结合这两种算法各自的优势,针对合成孔径雷达（Synthetic aperture radar,SAR）图像的特点,提出了一种基于上下文分析的无监督分层迭代算法. 该算法使用过分割区域作为操作单元,以提高分割速度,降低SAR图像相干斑噪声的影响. 在合并过分割区域时,该算法采用了分层迭代的策略:首先,设计了一种改进的模糊C均值聚类算法,对过分割区域的外观特征进行聚类分析,获得其类别标记,该类别标记包含了特征的分布结构信息. 然后,利用多种SAR图像特征对同类区域的空域上下文进行分析,使用区域迭代增长算法对全局范围内的相似区域进行合并,直到不存在满足合并条件的过分割区域对为止,再重新执行聚类算法. 这两种子算法分层交替迭代,扬长避短,实现了一种有效的方法来组织和利用多种信息对SAR图像进行分割. 对模拟和真实SAR图像的实验表明,本文提出的算法能够在区域一致性和细节保留之间做到很好的平衡,准确地分割出各类目标区域,对相干斑噪声具有很强的鲁棒性.     

Perceptual hashing for SAR image segmentation

Image segmentation is an important step in the implementation of the interpretation of synthetic aperture radar (SAR) image due to speckle. This article proposes a SAR image segmentation method based on perceptual hashing. The new algorithm is divided into two phases. The first phase is to obtain initial regions with multi-thresholding based on histogram after reducing the speckle noise. The initial regions are used as input data. And the next phase is to merge regions according to the similarity between regions. In this phase, to segment SAR image effectively, the proposed hashing algorithm is used to obtain hash value and similarity between regions, which preserve the texture features of SAR images. In addition, we can obtain a smooth segmentation result by reducing the redundant information with principal component analysis. Furthermore, morphological methods are used to eliminate the uneven background in the segmentation results. These improvements make our algorithm more effective to segment the images with high speed. The experimental results of four real and one synthetic SAR images verify the efficiency of our algorithm.     

基于马尔可夫随机场的SAR图象目标分割

运动、静止目标获取与识别（MSTAR）计划表明，将合成孔径雷达（SAP）图象分割成目标、阴影和背景杂波区域对于从开放环境中进行目标识别是一种有效的手段。但是由于SAP图象所固有的斑点噪声的影响，传统的分割方法很难获得准确的分割。为此提出了一种基于MRF(Markov random field)模型的SAP图象分割算法。用MRF模型描述待分割图象的先验知识，利用最大似然（ML）估计从训练数据中获得图象各区域的先验概率分布，采用Bayes方法，在观测数据基础上，根据分割图象的后验分布所对应的MRF模型的条件概率，利用Metroplis采样器获得最大后验概率（MAP）准则下的图象分割。通过对MSTAR的样本目标图象应用该算法，结果表明它可以获得稳健和准确的分割效果。     

High-resolution SAR images segmentation using NSCT denoising and QIGA based parameters selection of PCNN model

The high-resolution synthetic aperture radar (SAR) images usually contain inhomogeneous coherent speckle noises. For the high-resolution SAR image segmentation with such noises, the conventional methods based on pulse coupled neural networks (PCNN) have to face heavy parameters with a low efficiency. In order to solve the problems, this paper proposes a novel SAR image segmentation algorithm based on non-subsampling Contourlet transform (NSCT) denoising and quantum immune genetic algorithm (QIGA) improved PCNN models. The proposed method first denoising the SAR images for a pre-processing based on NSCT. Then, by using the QIGA to select parameters for the PCNN models, such models self-adaptively select the suitable parameters for segmentation of SAR images with different scenes. This method decreases the number of parameters in the PCNN models and improves the efficiency of PCNN models. At last, by using the optimal threshold to binary the segmented SAR images, the small objects and large scales from the original SAR images will be segmented. To validate the feasibility and effectiveness of the proposed algorithm, four different comparable experiments are applied to validate the proposed algorithm. Experimental results have shown that NSCT pre-processing has a better performance for coherent speckle noises suppression, and QIGA-PCNN model based on denoised SAR images has an obvious segmentation performance improvement on region consistency and region contrast than state-of-the-arts methods. Besides, the segmentation efficiency is also improved than conventional PCNN model, and the level of time complexity meets the state-of-the-arts methods. Our proposed NSCT+QIGA-PCNN model can be used for small object segmentation and large scale segmentation in high-resolution SAR images. The segmented results will be further used for object classification and recognition, regions of interest extraction, and moving object detection and tracking.

     

基于多小波域SOT结构的SAR图像去噪与压缩算法

合成孔径雷达（SAR）图像固有的相干斑噪声严重影响了SAR图像的判读和进一步压缩处理,提出一种在多小波域将空间方向树（SOT）去噪与压缩相结合的SAR图像压缩算法。首先利用SOT对高频子带的多小波系数进行软阈值去噪,滤除相干斑噪声;然后采用改进的多级树集合分裂（SPIHT）算法编码形成嵌入式码流。利用大量的机载SAR图像对该算法进行了仿真验证,实验结果表明采用该算法进行SAR图像压缩提高了重建图像的PSNR,同时对相干斑噪声进行了有效的抑制。     

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.     

IRGS: image segmentation using edge penalties and region growing

This paper proposes an image segmentation method named iterative region growing using semantics (IRGS), which is characterized by two aspects. First, it uses graduated increased edge penalty (GIEP) functions within the traditional Markov random field (MRF) context model in formulating the objective functions. Second, IRGS uses a region growing technique in searching for the solutions to these objective functions. The proposed IRGS is an improvement over traditional MRF based approaches in that the edge strength information is utilized and a more stable estimation of model parameters is achieved. Moreover, the IRGS method provides the possibility of building a hierarchical representation of the image content, and allows various region features and even domain knowledge to be incorporated in the segmentation process. The algorithm has been successfully tested on several artificial images and synthetic aperture radar (SAR) images.     

A segmentation algorithm for SAR images based on the anisotropic heat diffusion equation

A method toward unsupervised segmentation of synthetic aperture radar (SAR) images is proposed. In this method, the distribution of SAR intensity image and the maximum a posteriori (MAP) algorithm is used to obtain an initial segmentation. Then according to the equivalence between the solid heat diffusion model and image scale-space, multiscale anisotropic smoothing of the posterior probability matrixes is introduced to remove the influence of speckle and to preserve important structure information. The effectiveness of this algorithm is demonstrated by application to simulated and real SAR images.     

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.     

Multiregion level-set partitioning of synthetic aperture radar images

The purpose of this study is to investigate synthetic aperture radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.     

基于层次Gamma混合模型的高分辨率SAR影像分割方法

为了准确地对高分辨率合成孔径雷达(SAR)影像内像素强度统计分布建模并得到高精度的分割结果,提出基于层次Gamma混合模型(HGaMM)的高分辨率SAR影像分割方法.HGaMM由多个Gamma混合模型构成,用于对非对称、重尾和多峰等复杂的像素强度统计分布建模.为了减少影像噪声对分割的影响,采用马尔科夫随机场建模像素标号场,将像素邻域位置关系引入HGaMM.根据贝叶斯理论,利用后验分布构建影像分割模型.马尔科夫链蒙特卡罗算法用于模拟影像分割模型.在模拟和真实SAR影像上的分割实验表明,文中方法可得到较高精度的分割结果.     

基于引导滤波和Tsallis熵的SAR图像分割算法

针对合成孔径雷达(SAR)图像易受噪声干扰、分割方法精度低的问题,提出了一种基于频域引导滤波和Tsallis熵的SAR图像多阈值分割算法.利用非下采样Contourlet变换(NSCT)对图像多尺度分解,提取图像各方向的高频信息;通过引导滤波增强高频分量的边缘信息,在保持边缘的同时抑制了相干斑噪声;利用改进的二维Tsallis熵多阈值对增强图像精确分割.实验结果表明:分割算法对噪声不敏感,分割精度和适应性明显提高.     

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.     

Watershed-based hierarchical SAR image segmentation

Watershed transformation is a powerful image segmentation tool recently developed in mathematical morphology. In order to segment images initially oversegmented by watershed transformation, two approaches are considered: one is the thresholding of the gradient image proposed by us which is capable of keeping more salient image contours; the other is the well known centroid linkage region growing algorithm which merges regions with certain statistical similarities. By choosing suitable thresholds in the two approaches, hierarchical image segmentation algorithms can be constructed. A Ratio of Averages (ROA) edge detector is proposed to replace the morphological edge detectors prior to watershed transformation when applied to Synthetic Aperture Radar (SAR) images. Applications to SAR agricultural image segmentation with these hierarchical segmentation algorithms are presented. It is demonstrated that the algorithms are efficient in the segmentation of the SARimages and appropriate for land use applications when the land cover is made up of individual plots.     

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.     

区域GMM聚类的SAR图像分割

高斯混合模型(GMM)聚类算法近年来广泛应用于图像分割领域。但在SAR图像分割中,由于忽略了图像像素间的空间相关性,使其对相干斑噪声十分敏感。提出一种基于区域的GMM聚类算法,它将空间相关性引入聚类分类中,利用分水岭分割得到基本同质区域,计算区域的灰度均值作为GMM聚类算法的输入样本,将聚类特征从像素水平提升到区域水平,减少了噪声对分割结果的影响;并将自身反馈机制引入期望最大化(EM)算法中,进一步提高了GMM模型参数估计的精度。还对合成图像和真实SAR图像进行了分割实验,结果表明新算法可有效地提高分割的     

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。     

Functional template-based SAR image segmentation

We present an approach to automatic image segmentation, in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. In our approach, image segmentation is guided by a genetic algorithm which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce a candidate segmentation. The performance of each candidate segmentation is evaluated within the genetic algorithm, by a comparison to two physics-based techniques for region growing and edge detection. Through the process of segmentation, evaluation, and recombination, the genetic algorithm optimizes functional template design efficiently. Results are presented on real synthetic aperture radar (SAR) imagery of varying complexity.     

基于Chan-Vese模型的SAR图像分割

由于SAR图像存在较强的斑点噪声,使用Chan-Vese模型水平集分割方法会产生很多误分割。同时,水平集解法存在计算量大、分割速度慢的问题。在Chan-Vese模型基础上,增加新的内能项——距离正则项,得到了一种改进的曲线演化模型。避免了水平集函数的周期性更新,具有更大的迭代步长,从而加快分割速度,并且提高Chan-Vese模型的抗噪性。对该模型采用人工合成图像和真实SAR图像进行分割实验,通过比较,可看出改进模型具有较高的数值精度和较快的分割速度。对于噪声很强的图像,使用增强Lee滤波进行预处理,可以进一步提高改进模型的分割速度和效果。实验结果表明:改进Chan-Vese模型能高效快速地完成SAR图像分割,具有较高的抗噪性。     

一种基于纹理特征融合的SAR图像分割方法

在由若干灰度共生矩阵纹理统计量进行特征融合后所生成的图像上,定义多分辨双Markov-GAR模型,采用多分辨MPM参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法既利用了像素的灰度信息,也利用了像素的空间位置信息,削弱了斑点噪声对分割的影响。实验表明对于一些高分辨SAR图像,该方法与单纯基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,分割精度得以提高。