Supervised segmentation of remote sensing images based on a tree-structured MRF model

Most remote sensing images exhibit a clear hierarchical structure which can be taken into account by defining a suitable model for the unknown segmentation map. To this end, one can resort to the tree-structured Markov random field (MRF) model, which describes a K-ary field by means of a sequence of binary MRFs, each one corresponding to a node in the tree. Here we propose to use the tree-structured MRF model for supervised segmentation. The prior knowledge on the number of classes and their statistical features allows us to generalize the model so that the binary MRFs associated with the nodes can be adapted freely, together with their local parameters, to better fit the data. In addition, it allows us to define a suitable likelihood term to be coupled with the TS-MRF prior so as to obtain a precise global model of the image. Given the complete model, a recursive supervised segmentation algorithm is easily defined. Experiments on a test SPOT image prove the superior performance of the proposed algorithm with respect to other comparable MRF-based or variational algorithms.     

A multiscale random field model for Bayesian image segmentation

Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.     

Segmentation of Gabor-filtered textures using deterministicrelaxation

A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing.     

Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number

In view of the traditional Gaussian mixture model (GMM),it was difficult to obtain the number of classes and sensitive to the noise.A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed.First,in the built GMM,prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF).In order to improve the sensitivity of noise,the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels.For estimating the number of classes and the parameters of model,the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed,respectively.Finally,by minimizing the smoothing factor the final segmentation was obtained.In order to verify the proposed segmentation method,the synthetic and real panchromatic images were tested.The experimental results show that the proposed method is feasible and effective.     

基于纹元森林和显著性先验的弱监督图像语义分割方法

弱监督语义分割任务常利用训练集中全体图像的超像素及其相似度建立图模型,使用图像级别标记的监督关系进行约束求解。全局建模缺少单幅图像结构信息,同时此类参数方法受到复杂度限制,无法使用大规模的弱监督训练数据。针对以上问题,该文提出一种基于纹元森林和显著性先验的弱监督图像语义分割方法。算法使用弱监督数据和图像显著性训练随机森林分类器用于语义纹元森林特征(Semantic Texton Forest, STF)的提取。测试时,先将图像进行过分割,然后提取超像素语义纹元特征,利用朴素贝叶斯法进行超像素标记的概率估计,最后在条件随机场(CRF)框架下结合图像显著性信息定义了新的能量函数表达式,将图像的标注(labeling)问题转换为能量最小化问题求解。在MSRC-21类数据库上进行了验证,完成了语义分割任务。结果表明,在并未对整个训练集建立图模型的情况下,仅利用单幅图像的显著性信息也可以得到较好的分割结果,同时非参模型有利于规模数据分析。     

A tree-structured Markov random field model for Bayesian image segmentation

We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.     

一种采用高斯隐马尔可夫随机场模型的遥感图像分类算法

该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概率分布,使用EM(Expectation-Maximization)算法解决从不完整数据中估计概率模型参数问题。针对遥感图像分布的不均匀特性,该文提出的算法没有采用固定的马尔可夫随机场模型参数,而是在递归分类算法中分级地调整模型参数以适应区域的变化。实验结果表明了该文算法的有效性,分类算法处理精度高于C-Means聚类算法.。     

Generative bayesian image super resolution with natural image prior

We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms.     

基于扩散方程和MRF的SAR图像分割

该文提出了一种基于图像扩散方程和马尔科夫随机场(MRF)的合成孔径雷达(SAR)图像分割方法。在传统MRF算法的基础之中,引入对图像的扩散,用来平滑SAR图像中的噪声,保护图像中的边缘部分,并且加快收敛的速度。首先对输入的SAR图像进行扩散,通过MRF进行统计,得到图像中各点的后验概率,再对得到的后验概率进行扩散。与传统的MRF算法进行比较,该文的方法较好地去除了误分割斑块,减少算法的运行时间。     

基于EM参数“软”估计的SAR图像分割

借助EM算法和模糊理论,提出了一种基于参数"软"估计和Markov随机场的SAR图像无监督分割方法。首先利用多维空间的EM算法估计随机场的模型参数,并根据随机场模型参数分别计算观测数据的条件概率和标记图像的先验概率,继而根据最大后验概率准则将图像分成具有相似统计特性的同质区域,重复以上步骤直至收敛。通过与传统的参数"硬"估计分割算法的实验比较,该算法能更好保持图像边缘细节,区域连通性更好。     

基于区间二型模糊神经网络的高分辨率遥感影像分割方法

高分辨率遥感影像同质区域地物目标异质性增大,光谱测度空间复杂性增加使像素类属的不确定性以及分割决策不确定性增大,引起分割精度下降。提出一种基于区间二型模糊神经网络的高分辨率遥感影像监督分割方法。对同质区域构建一型高斯隶属函数模型刻画像素类属的不确定性;模糊化高斯隶属函数参数构建区间二型模糊模型处理分割决策的不确定性;以训练样本在所有类别中的一型模糊隶属度及上、下隶属度为输入,建立模糊神经网络模型并融入像素邻域关系作为模糊决策。采用文中算法、FCM方法、HMRF-FCM及区间二型模糊神经网络方法分别对合成影像及真实高分辨遥感影像进行分割,定性与定量的对比分析验证了文中算法具有更高的分割精度。      

基于MRF模型的可靠的图像分割

本文提出一种可靠的图象分割算法。基于实际图象是分割图像叠加了不规则噪声的假设,用ＭＦＲ模型描述分割图象的先验分布,用被污染的高斯分布描述待分割的图像。采用Ｂａｙｅｓ方法,根据分割图像的后验分布所对应的ＭＲＦ模型的条件概率,用ＩＣＭ局部优化方法,获得ＭＡＰ准则下的图像分割结果。该算法与Ｌａｋｓｈｍａｎａｎ等提出的算法相比,具有更好的可靠性,实验结果是令人满意的。     

模糊区域级MRF方法在城镇自动识别中的应用

针对遥感影像中城镇区域内外的自然地物难以区分,城镇区域不易完整识别的问题,提出一种对象级模糊MRF识别方法.该方法首先通过光谱信息和空间梯度分析得到城镇种子点（人造地物顶部点和阴影点）;然后由均值漂移算法过分割影像;再对过分割区域建立MRF,在迭代过程中用MRF的条件概率矩阵代替模糊C均值聚类算法的隶属度矩阵,并保持包含种子点的区域类别不变,从而实现城镇识别.对于QuickBird和Ikonos遥感影像,该模型能够兼顾城镇区域自动识别过程中的随机性与模糊性,很好地利用了空间相关信息,有效识别出了城镇区域.     

基于采样协方差矩阵的混合核SVM高效频谱感知

近年来随着盲检测算法的提出,越来越多的基于采样协方差矩阵的盲检测算法应用于频谱感知。针对其检测门限是近似值,检测性能会受到影响等问题,提出了基于采样协方差矩阵的混合核函数的支持向量机（support vector machine,SVM）高效频谱感知,通过感知信号采样协方差矩阵的最大最小特征值（maximum minimum eigenvalue,MME）和协方差绝对值（covariance absolute value,CAV）提取的统计量作为SVM的特征向量并训练其生成频谱感知的分类器,无需计算检测门限并且特征提取减少了样本集的大小。利用遗传算法（genetic algorithm,GA）优化混合核函数的SVM的参数。实验结果表明,该方法比MME算法和CAV算法的检测概率有所提高,并且比SVM减少了感知时间,具有良好的实用性。     

Colorization for Gray Scale Facial Image by Locality-Constrained Linear Coding

Colorization for gray scale facial image is an important technique in various practical applications. However, the methods that have been proposed are essentially semi-automatic. In this paper, we present a new probabilistic framework based on Maximum A Posteriori (MAP) estimation to automatically transform the given gray scale facial image to corresponding color one. Firstly, the input image is divided into several patches and non-parametric Markov random field (MRF) is employed to formulate the global energy. Secondly, Locality-constrained Linear Coding (LLC) is employed to learn the color distribution for each patch. At the same time, the simulated annealing algorithm is employed to iteratively update the patches chosen by LLC to optimize the MRF by decreasing global energy cost. The experimental results demonstrate that the proposed framework is effective to colorize the gray scale facial images to corresponding color ones.     

基于生成式对抗网络的遥感图像半监督语义分割

高分辨率遥感图像的语义分割问题是目前遥感图像处理领域中的研究热点之一。传统的有监督分割方法需要大量的标记数据,而标记过程又较为困难和耗时。针对这一问题,提出一种基于生成式对抗网络的半监督高分辨率遥感图像语义分割方法,只需要少量样本标签即可得到较好的分割结果。该方法为分割网络添加全卷积形式的辅助对抗网络,以助于保持高分辨率遥感图像分割结果中的标签连续性;更进一步,提出一种新颖的能够进行注意力选择的对抗损失,以解决分割结果较好时判别器约束的分割网络更新过程中存在的难易样本不均衡问题。在ISPRS Vaihingen 2D语义标记挑战数据集上的实验结果表明,与现有其它语义分割方法相比,所提出方法能够较大幅度地提高遥感图像的语义分割精度。     

基于Word Embedding的遥感影像检测分割

遥感影像检测分割技术通常需提取影像特征并通过深度学习算法挖掘影像的深层特征来实现.然而传统特征（如颜色特征、纹理特征、空间关系特征等）不能充分描述影像语义信息,而单一结构或串联算法无法充分挖掘影像的深层特征和上下文语义信息.针对上述问题,本文通过词嵌入将空间关系特征映射成实数密集向量,与颜色、纹理特征的结合.其次,本文构建基于注意力机制下图卷积网络和独立循环神经网络的遥感影像检测分割并联算法（Attention Graph Convolution Networks and Independently Recurrent Neural Network,ATGIR）.该算法首先通过注意力机制对结合后的特征进行概率权重分配;然后利用图卷积网络（GCNs）算法对高权重的特征进一步挖掘并生成方向标签,同时使用独立循环神经网络（IndRNN）算法挖掘影像特征中的上下文信息,最后用Sigmoid分类器完成影像检测分割任务.以胡杨林遥感影像检测分割任务为例,我们验证了提出的特征提取方法和ATGIR算法能有效提升胡杨林检测分割任务的性能.     

基于多特征组合的彩色遥感图像分类研究

为了解决利用单一特征对彩色遥感图像进行分类效果不理想、普适性不强等问题,提出了一种基于颜色和纹理特征组合的支持向量机彩色遥感图像分类方法。该方法尝试将彩色遥感图像的颜色信息和纹理信息相结合作为支持向量机算法分类的特征向量,据此对遥感影像进行分类,并进行了实验验证。结果表明,颜色和纹理特征组合的支持向量机分类方法能够取得较高的分类精度,其分类效果优于传统的单一颜色或纹理特征分类,是一种有效的彩色遥感图像分类方法。     

Image segmentation using hidden Markov Gauss mixture models.

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.     

基于Voronoi几何划分和层次化建模的纹理影像分割

将基于像素MRF分割方法拓展到基于地物目标几何约束的区域MRF分割,提出了一种基于区域和统计的纹理影像分割方法,其基本思想是利用Voronoi划分技术将影像域划分为若干子区域。在此基础上,采用二值高斯马尔科夫随机场（BGMRF, bivariate gaussian markov random field）模型,静态随机场模型和Potts模型从邻域、区域及全局层次描述影像的纹理结构,并将该纹理结构模型纳入贝叶斯框架;依据贝叶斯定理构建纹理影像分割模型;利用metropolis-hastings (M-H)算法进行模型参数估计,并依据最大后验概率（MAP, maximum a posterior）准则进行优化,从而完成纹理影像分割。为了验证所提出方法的正确性,分别对合成纹理影像,真实纹理影像及遥感影像进行了分割实验,定性和定量的测试结果验证了提出方法的有效性、可靠性和准确性。