自动确定类别数的RJMCMC+SA图像分割算法研究

在保证遥感图像分割模型复杂性、分割精度的情况下,自动确定分割类别数是一 个重点问题。为此,结合可逆跳马尔科夫蒙特卡洛和模拟退火理论(RJMCMC+SA)构建了图像 分割算法。通过高斯曲率滤波(GC)对图像进行几何平滑处理,依据贝叶斯理论形式化非线性回 归模型中的参数变量从而建立后验概率分布,利用 RJMCMC 算法实现该后验概率分布并构建 概率转移核,结合 SA 算法加速概率转移核收敛,确定分割算法中径向基函数的个数和参数, 完成类别数自动确定和图像全局性分割。在全色遥感图像和伯克利大学实验数据库图像上,分 别与 4 种径向基函数分割模型实验对比,数据分析表明,该算法不仅在复杂性和精确度上取得 很好的平衡,而且能够自动确定图像类别数。     

基于最小描述长度原则的各向异性扩散模型

各向异性扩散的最大特点在于它是有选择性的平滑过程,这种平滑过程在均匀的区域不受限制,而在跨越边界部分被抑制,因此噪声和一些无关的细节被平滑掉了,从而能够有效地实现图像保边缘平滑。在现有各向异性扩散模型中,偏微分扩散方程解的适定性和扩散系数中的梯度阈值的合理估计是尚未很好解决的问题。为此利用最小描述长度(MDL)原则发展了一种各向异性扩散模型,并与Lyapunov函数的p-范数相结合,改善了各向异性扩散模型中梯度阈值的估计方法,形成了一种性能较好的各向异性扩散非线性滤波技术。实验结果表明,该方法不仅能够更有效地识别噪声图像中的细节边缘,而且还保证了各向异性扩散模型的稳定性;改进的扩散模型,滤波效果优于传统的各向异性扩散模型,是一种较为理想的保边缘滤波方法。     

基于时空结合概率模型的运动估计和分割方法

探讨了一种基于贝叶斯框架的时空标记场最大后验边缘概率与最大后验概率相结合的运动对象分割算法.通过建立贝叶斯分布模型,求得对象分割标记场的最大后验概率,引入最大后验边缘概率求取最小能量.该算法将时间域分割结果作为初始标记场,空间域的分割结果作为图像的观察场,获取初始运动数目以及相应的运动模型的初始参数,然后通过参数估计,不断更新模型参数,之后通过把每个运动区域和运动模型相关联,来估计运动区域,最终达到分割的目的.实验结果证明,研究的方法对运动目标分割具有较好的分割效果.     

基于对数压缩的超声各向异性扩散去噪方法

针对当前超声图像去噪算法很难同时做到降噪和边缘保持的情况,在进行各向异性扩散模型研究的基础上,提出基于对数压缩的改进各向异性扩散算法（LCAD）去除超声散斑噪声。算法将图像对数压缩后进行噪声分布模型估计,然后构造基于广义伽马分布的扩散系数,在扩散过程中达到降噪和边缘保持效果。     

基于多尺度边缘保持正则化的超分辨率复原

超分辨率复原是一种由一序列低分辨率变形图像来估计一幅(或一序列)较高分辨率的非变形图像的技术,同时,它能够消除加性噪声以及由有限检测器尺寸和光学元件产生的模糊.提出了一种基于多尺度正则化先验的最大后验概率超分辨率复原算法.算法特点如下:(1) 对运动估计结果实施可信度验证;(2) 采用图像的多尺度小波表征来定义图像的空域活动性测度,并由此构建多尺度Huber-Markov先验模型.实验结果表明,该算法不仅具有较好的超分辨率图像边缘保持能力,而且能够有效地消除图像伪迹.该算法可以应用于遥感图像、医学成像、高清晰度电视标准和合成视频变焦等领域.     

结合自适应保真项的各向异性扩散模型

为了在去除噪声的同时,对图像更好的保真,在各向异性扩散模型的基础上,提出了结合自适应保真项的各向异性扩散模型。该模型能够很好地抑制边缘上的噪声和强噪声。实验结果表明,该模型不仅能有效去除噪声,而且对图像细节、边缘也能很好的保真。     

基于异性扩散-中值滤波的超声医学图像去噪方法

针对超声图像存在一种特殊的斑点噪声,使图像边界与细节变得模糊而严重影响图像质量的问题,提出了一种新的去除医学图像斑点噪声的方法,它利用中值滤波和各向异性扩散相结合,不仅可以有效地去除噪声而且很好地保持了边缘、局部细节信息.此外,该方法在扩散过程中,梯度阈值选取的不同对图像结果影响很小,这极大地提高了该算法的健壮性.实验中,通过和各向异性扩散、中值滤波等方法的比较,表明该方法具有良好的去噪效果.     

二元树复小波域的局部高斯混合模型图像降噪

在复小波域上对观测图像进行一种基于高斯混合模型的后验概率分类,并在每类小波系数的局部邻域估计出局部高斯混合模型的参数,这种参数估计是局部自适应的;然后用该局部高斯混合模型对各个子带系数进行贝叶斯框架下的最大后验概率(MAP)估计,以达到降低噪声的目的．由于这种小波变换具有近似的平移不变性和良好的方向选择性,因此在降噪的同时可以很好地消除主要边缘处的“震铃”效应．实验结果表明;文中算法无论从峰值信噪比还是从主观视觉效果上都要优于一些传统的降噪算法．     

基于解剖非局部先验的模糊扩散PET重建算法

针对传统最大后验(MAP)算法出现阶梯伪影以及不能有效保持重建图像低梯度值处细节信息的问题,提出了一种基于解剖非局部先验的模糊扩散正电子发射计算机断层扫描(PET)重建算法。首先,对中值先验分布的MAP重建进行改进,在每次中值滤波前引入结合模糊函数的各向异性扩散滤波器;然后,采用模糊隶属度函数作为各向异性扩散过程的扩散系数,并结合解剖非局部先验来考虑图像的细节信息。仿真结果表明,与传统算法相比,该算法提高了信噪比(SNR),具有良好的抗噪性;同时视觉效果较好,图像边缘清晰,在抑制噪声和边缘保持方面取得了良好的折中。     

基于MAP-MRF的旋转3维超声心动图断层重建及降噪

提出了一种基于最大后验概率准则(MAP)-马尔可夫随机场(MRF)的3维超声图像重建算法,在3维重建的过程中可以有效地去除超声图像特有的斑点噪声。首先提出了3维重建的数学模型,它假设所要估计的空间灰度值属于某个函数向量空间,这个向量空间的基函数因支持区域的不同而不同,将Rayle igh分布和G ibbs分布作为先验知识,通过后验概率最大化(MAP)来估计基函数前的系数,在重建算法中,通过将邻点联系强度α设定为随梯度的变化而变化,从而达到了保边界的各向异性滤波特性。采用ICM算法求解这个系数向量。为了提高该算法的效率,又进一步提出了其改进算法,最后的实验结果显示,这种基于MAP-MRF的重建算法可以有效地去除超声图像中的斑点噪声。     

高斯Copula的多光谱遥感影像分割

为了充分利用多光谱影像波段间的相关性,提出高斯Copula的多光谱遥感影像分割方法.首先,建立基于马尔可夫随机场的标号场模型,使用Potts模型刻画该标号场.然后,建立表征像素光谱测度的特征场,利用高斯Copula建立像素光谱测度的多变量统计模型以刻画该特征场.结合标号场、特征场模型及各模型参数的先验概率,利用贝叶斯定理建立多光谱影像分割的后验概率模型.最后,设计适用于模拟后验概率模型的M-H算法,在最大后验概率策略下获取最优分割结果.对模拟和真实多光谱影像分割结果表明,文中方法描述波段间相关性的能力较强,准确性较高.     

ICA mixture model algorithm for unsupervised classification of remote sensing imagery

Conventional remote sensing classification algorithms assume that the data in each class can be modelled using a multivariate Gaussian distribution. As this assumption is often not valid in practice, conventional algorithms do not perform well. In this paper, we present an independent component analysis (ICA)‐based approach for unsupervised classification of multi/hyperspectral imagery. ICA used for a mixture model estimates the data density in each class and models class distributions with non‐Gaussian (sub‐ and super‐Gaussian) probability density functions, resulting in the ICA mixture model (ICAMM) algorithm. Independent components and the mixing matrix for each class are found using an extended information‐maximization algorithm, and the class membership probabilities for each pixel are computed. The pixel is allocated to the class having maximum class membership probability to produce a classification. We apply the ICAMM algorithm for unsupervised classification of images obtained from both multispectral and hyperspectral sensors. Four feature extraction techniques are considered as a preprocessing step to reduce the dimensionality of the hyperspectral data. The results demonstrate that the ICAMM algorithm significantly outperforms the conventional K‐means algorithm for land cover classification produced from both multi‐ and hyperspectral remote sensing images.     

一种整合单通道各向异性扩散信息的水平集图像分割方法*

在对Chan-Vese提出的基于简化Mumford-Shah模型(C-V模型)改进的基础上,针对彩色图像、多光谱图像等多通道图像,提出了一种多通道C-V模型水平集图像分割方法.首先将多通道图像分解到各单通道,使用一种新的各向异性扩散方法对各通道进行平滑滤波,然后使用能够整合各通道各向异性扩散信息的多通道C-V模型进行分割.普通彩色图像与多光谱图像数据的实验结果表明,该方法分割质量明显优于传统的C-V模型分割.     

基于高斯混合模型的遥感影像连续型朴素贝叶斯网络分类器

提出了一种新的嵌入高斯混合模型(GMM,Gaussian Mixture Model)遥感影像朴素贝叶斯网络模型GMM-NBC(GMMbased Na ve Bayesian Classifier)。针对连续型朴素贝叶斯网络分类器中假设地物服从单一高斯分布的缺点,该方法将地物在特征空间的分布用高斯混合模型来模拟,用改进EM算法自动获取高斯混合模型的参数;高斯混合模型整体作为一个子节点嵌入朴素贝叶斯网络中,将其输出作为节点(特征)的中间类后验概率,在朴素贝叶斯网络的框架下进行融合获得最终的类后验概率。对多光谱和高光谱数据的分类实验结果表明,该方法较传统贝叶斯分类器分类效果要好,且有较强的鲁棒性。     

Blind spatial unmixing of multispectral images: New methods combining sparse component analysis,clustering and non-negativity constraints

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of these surface components. In such situations, classical classification is not acceptable for many major applications, such as environmental monitoring, agriculture, mineral exploration and mining, etc. Most methods proposed for treating this problem have been developed for hyperspectral images. On the contrary, there are very few automatic techniques suited to multispectral images. In this paper, we propose new unsupervised spatial methods (called 2D-Corr-NLS and 2D-Corr-NMF) in order to unmix each pixel of a multispectral image for better recognizing the surface components constituting the observed scene. These methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA), clustering and non-negativity constraints. Our approach consists in first identifying the mixing matrix involved in this BSS problem, by using the first stage of a spatial correlation-based SCA method with very limited source sparsity constraints, combined with clustering. Non-negative least squares (NLS) or non-negative matrix factorization (NMF) methods are then used to extract spatial sources. An important advantage of our proposed methods is their applicability to the possibly globally underdetermined, but locally (over)determined BSS model in multispectral remote sensing images. Experiments based on realistic synthetic mixtures and real multispectral images collected by the Landsat ETM+ and the Formosat-2 sensors are performed to evaluate the performance of the proposed approach. We also show that our methods significantly outperform the sequential maximum angle convex cone (SMACC) method.     

面向遥感图像无损压缩的CCD噪声抑制方法研究

CCD在成像过程中所产生的噪声会限制星地间遥感图像数据的传输效率。因此,为有效抑制CCD噪声并提高遥感图像的无损压缩比,本文提出了一种基于图像噪声标准差估计的各向异性扩散方法。首先,结合图像同质性测度和边缘提取结果提出一种遥感图像噪声标准差的自动化估计方法。而后,通过噪声模拟的方式拟合出图像噪声标准差与经典式各向异性扩散方程传导系数中梯度阈值的线性关系。基于图像噪声标准差估计和同质性测度结果,最终提出一种可自适应性调整传导系数值和迭代次数的噪声驱动式各向异性扩散方法。本文中分别采用北京一号小卫星多光谱遥感图像和标准测试图像对所提出的噪声估计方法和噪声抑制方法进行评价。试验结果表明,对比其他各向异性扩散方法,本文所提出的方法不仅可实现更好的图像复原效果,还可有效地提高多光谱遥感图像的无损压缩比,并为今后星上数据处理系统的研究提供一种较新的思路。     

Remote Sensing: Statistical Testing of Thematic Map Accuracy

In order to achieve wider acceptance among users of thematic maps derived from remote sensing data, the interpreter must be able to specify the accuracy of his product. This requires a valid sampling procedure to estimate classification accuracy. Although several alternative methods have been used in the past, none provide sufficient statistical justification for the allocation of sample points in each category of land use using remote sensing imagery. This paper describes a more detailed and more reliable method for determining the most appropriate (i.e., minimum,) sample size. The concept developed and described in the paper incorporates the probability of making incorrect interpretations at particular prescribed accuracy levels, for a certain number of errors, for a particular sample size. The remote sensing sampling strategy presented has the added advantage that it can easily be adapted for use with most forms of remote sensing imagery, including orbital data. It provides a reliable framework for testing the accuracy of any remote sensing image interpretation — based land use classification using the minimum number of sample points; thereby saving time and money, especially if it is employed in operational surveys where high specification accuracy levels need to be guaranteed.     

基于独立分量分析和支持向量机的遥感影像融合分类算法

遥感影像分类是遥感定量化分析的重要手段,遥感影像融合是提高分类正确率的有效途径之一。本文提出一种遥感影像的融合分类算法。首先采用Contourlet变换对多光谱影像和全色影像进行融合,然后结合独立分量分析的去相关性、稀疏特性以及很好地捕捉影像重要边缘信息、纹理信息的能力,提取融合影像的独立分量特征,并用支持向量机实现分类。与其他算法的主、客观比较结果表明,该算法的实验效果较好,可有效地提高遥感影像的分类精度。     

多光谱遥感影像的SAM-SID混合分类技术研究

以SPOT 5多光谱影像为数据源,通过与SAM、SID以及常规的最大似然法(ML)和最小距离法(MD)的对比,研究了基于SAM-SID混合法的土地覆盖多光谱遥感分类技术。研究结果显示,相比于SAM和SID,SID(TAN)和SID(SIN)两个SAM-SID混合参量对多光谱影像上地物识别的能力更强,尤以SID(SIN)的识别能力最强;基于SID(SIN)的多光谱遥感分类验证精度达78.94%,不但明显高于SAM和SID法,而且也高于常规的MD和ML监督分类方法。这说明SAM-SID混合分类方法不但适用于高光谱遥感分类,同时在多光谱遥感分类中也有很强的适用性。     

Geomorphologic mapping from hyperspectral data,using Gaussian mixtures and lower confidence bounds

Probabilistic classification under the Gaussian mixture model is normally based on posterior probability (p.p.) estimates of class membership. The question, how accurate they are for a given pixel, is traditionally left without attention, which may lead to unreasonable optimism about the classification results obtained. Addressing the issue, Koltunov and Ben‐Dor have proposed an unsupervised, lower confidence bound (l.c.b.)‐based method for thematic interpretation of remote sensing data. This method predicts the sampling properties of the p.p. estimators of a given pixel, to assess reliability of the estimates. The present paper describes a modified version of the method. In particular, instead of defining the l.c. bounds in terms of two first moments of the sampling distribution, as has been suggested previously, we use percentiles. Combining this with a probabilistic model of supervised identification of the mixture components yields the post‐classification uncertainty value for a given pixel and the confidence level, at which this value is proven to be maximal. In the application to an arid landscape in the Southern Negev desert, Israel, the compressed raw hyperspectral data acquired by the Digital Airborne Imaging Spectrometer (DIAS‐7915) was clustered once, whereas two thematic tasks were solved corresponding to different map legends, identification procedures, and the associated requirements to the level of detail and reliability of the thematic maps. The reference data collected in the field have provided evidence for accurate algorithmically estimated confidence bounds of the classification quality. The classification has revealed new information about the geomorphological subunits forming the study area.