Endmember variability in Spectral Mixture Analysis: A review

The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.     

Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions

We propose a robust Poisson geometric process model with heavy-tailed distributions to cope with the problem of outliers as it may lead to an overestimation of mean and variance resulting in inaccurate interpretations of the situations. Two heavy-tailed distributions namely Student’s t and exponential power distributions with different tailednesses and kurtoses are used and they are represented in scale mixture of normal and scale mixture of uniform respectively. The proposed model is capable of describing the trend and meanwhile the mixing parameters in the scale mixture representations can detect the outlying observations. Simulations and real data analysis are performed to investigate the properties of the models.     

Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling

In some biological experiments, it is quite common that laboratory subjects differ in their patterns of susceptibility to a treatment. Finite mixture models are useful in those situations. In this paper we model the number of components and the component parameters jointly, and base inference about these quantities on their posterior probabilities, making use of the reversible jump Markov chain Monte Carlo methods. In particular, we apply the methodology to the analysis of univariate normal mixtures with multidimensional parameters, using a hierarchical prior model that allows weak priors while avoiding improper priors in the mixture context. The practical significance of the proposed method is illustrated with a dose-response data set.     

A statistical approach to high-throughput screening of predicted orthologs

Orthologs are genes in different species that have diverged from a common ancestral gene after speciation. In contrast, paralogs are genes that have diverged after a gene duplication event. For many comparative analyses, it is of interest to identify orthologs with similar functions. Such orthologs tend to support species divergence (ssd-orthologs) in the sense that they have diverged only due to speciation, to the same relative degree as their species. However, due to incomplete sequencing or gene loss in a species, predicted orthologs can sometimes be paralogs or other non-ssd-orthologs. To increase the specificity of ssd-ortholog prediction, Fulton et al. [Fulton, D., Li, Y., Laird, M., Horsman, B., Roche, F., Brinkman, F., 2006. Improving the specificity of high-throughput ortholog prediction. BMC Bioinformatics 7 (1), 270] developed Ortholuge, a bioinformatics tool that identifies predicted orthologs with atypical genetic divergence. However, when the initial list of putative orthologs contains a non-negligible number of non-ssd-orthologs, the cut-off values that Ortholuge generates for orthology classification are difficult to interpret and can be too high, leading to decreased specificity of ssd-ortholog prediction. Therefore, we propose a complementary statistical approach to determining cut-off values. A benefit of the proposed approach is that it gives the user an estimated conditional probability that a predicted ortholog pair is unusually diverged. This enables the interpretation and selection of cut-off values based on a direct measure of the relative composition of ssd-orthologs versus non-ssd-orthologs. In a simulation comparison of the two approaches, we find that the statistical approach provides more stable cut-off values and improves the specificity of ssd-ortholog prediction for low-quality data sets of predicted orthologs.     

A flexible extreme value mixture model

Extreme value theory is used to derive asymptotically motivated models for unusual or rare events, e.g. the upper or lower tails of a distribution. A new flexible extreme value mixture model is proposed combining a non-parametric kernel density estimator for the bulk of the distribution with an appropriate tail model. The complex uncertainties associated with threshold choice are accounted for and new insights into the impact of threshold choice on density and quantile estimates are obtained. Bayesian inference is used to account for all uncertainties and enables inclusion of expert prior information, potentially overcoming the inherent sparsity of extremal data. A simulation study and empirical application for determining normal ranges for physiological measurements for pre-term infants is used to demonstrate the performance of the proposed mixture model. The potential of the proposed model for overcoming the lack of consistency of likelihood based kernel bandwidth estimators when faced with heavy tailed distributions is also demonstrated.     

Gamma mixture models for target recognition

This paper considers a mixture model approach to automatic target recognition using high-resolution radar measurements. The mixture model approach is motivated from several perspectives including requirements that the target classifier is robust to uncertainty in amplitude scaling, rotation and translation of the target. Estimation of the model parameters is achieved using the expectation-maximisation (EM) algorithm. Gamma mixtures are introduced and the re-estimation equations derived. The models are applied to the classification of high-resolution radar range profiles of ships and results compared with a previously published self-organising map approach.     

模糊综合评判中几种数学模型的比较

本文对模糊综合评判中几种常见的数学模型进行了比较,分析了这几种数学模型的实质,为模糊综合评判的合理应用提供了基础。     

带杂交算子的蚁群算法

蚁群算法是一种由意大利学者Macro Dorigo等提出的新型模拟进化算法,它具有许多优良性质,因此被广泛用于求解组合优化问题。但基本蚁群算法有许多不足。特别是许多搜索速度慢,且容易陷入局部最优。该文针对这个问题提出了一种改进算法。该算法通过引入遗传算法中用到的杂交算子来改善蚁群,使其对应的问题的解更加优良,用改进算法求解TSP问题的结果表明改进算法是有效的。     

聚类分析中特征选择的研究*

介绍了一种新颖的基于高斯混合模型的特征选择算法,并且应用该方法的结果对模拟数据和真实数据进行聚类。实验结果表明,该算法可以有效地确定显著属性,提高聚类准确度。     

一种双线性快速模糊增强图像边界检测最新算法*

在广义模糊集理论的基础上,给出了全新的用于模糊增强图像区域对比度的线性广义模糊算子,从而提出了一种双线性快速模糊增强图像边界检测最新算法。利用线性左半梯形模糊分布将灰度图像的普通空间集合变换为广义模糊空间集合,再利用LGFO对广义模糊集合进行区域对比度增强,把增强后的广义模糊集合变换为普通集合,最后对处理后的普通集合进行边界提取。实例表明该算法提取图像边界速度快、效果好。