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关键词： Supervised image classification   Refining training samples   Outlier detection   Median absolute deviation  

摘要： Supervised image classification refers to the task of extracting information classes from a multi-band remote sensing image. The selection of training samples is critical and directly influences supervised classification accuracy. However, some impure training samples are possible selected because of human mistakes or limited labeling conditions, which leads to a reduction in the classification accuracy. To solve this issue, median absolute deviation (MAD) is adopted to refine training samples. A comparison of the full and refined training samples is conducted for the same classifier, i.e., maximum likelihood classification (MLC) or support vector machine (SVM), through experimental evaluation with two sets of experiments. The results of experiments show that the overall accuracy and the kappa coefficient of the refined training samples significantly outperform those of the full training samples for the same classifier (MLC or SVM). It shows that refining training samples using the MAD can effectively eliminate the influence of impure training samples so that the more reliable and accurate results can be obtained.

关键词： Hyperspectral image classification   mixed pixels   mutinomial logistic regression (MLR)   spectral unmixing   subspace learning  

摘要： The imbalance between the (often limited) number of available training samples and the high data dimensionality, together with the presence of mixed pixels, often complicates the classification of remotely sensed hyperspectral data. In this paper, we tackle these problems by developing a new method that combines spectral unmixing and classification techniques in a subspace-based approach. The proposed method is developed under the assumption that the spectral signature of a land cover class is associated with a given set of pure spectral signatures (called endmembers in spectral unmixing terminology), which define a low-dimensional subspace with clear physical meaning. We aim to exploit this relationship to learn the class-dependent subspaces and integrate them with a multinomial logistic regression procedure. Experiments on synthetic datasets and real hyperspectral images show that our method is able to obtain competitive performances in comparison with other approaches, particularly when very limited training sets are available.

关键词： Convolutional neural networks (CNNs)   hyperspectral image (HSI) classification   multilayer feature fusion   nonlocal information   spatial-spectral feature extraction  

摘要： The extraction of joint spatial-spectral features has been proved to improve the classification performance of hyperspectral images (HSIs). Recently, utilizing convolutional neural networks (CNNs) to learn joint spatial-spectral features has become of great interest. However, the existing CNN models ignore complementary spatial-spectral information among the shallow and deep layers. Moreover, insufficient training samples in HSIs afflict these CNN models with overfitting problem. In order to address these problems, a novel CNN method for HSI classification is proposed. It considers multilayer spatial-spectral feature fusion and sample augmentation with local and nonlocal constraints, which is abbreviated as MSLN-CNN. In MSLN-CNN, a triple-architecture CNN is constructed to extract spatial-spectral features by cascading spectral features to dual-scale spatial features from shallow to deep layers. Then, multilayer spatial-spectral features are fused to learn complementary information among the shallow layers with detailed information and the deep layers with semantic information. Finally, the multilayer spatial-spectral feature fusion and classification are integrated into a unified network, and MSLN-CNN can be optimized in the end-to-end way. To alleviate the small sample size problem, the unlabeled samples having high confidences on local spatial constraint and nonlocal spectral constraint are selected and prelabeled. The nonlocal spectral constraint considers the structure information with spectrally similar samples in the nonlocal searching, while the local spatial con-straint utilizes the contextual information with spatially adjacent samples. Experimental results on several hyperspectral datasets demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods, especially with the limited training samples.

关键词： 糖尿病肾病   早期诊断   生化检验指标  

摘要： 分析糖尿病肾病早期诊断中生化检验指标的作用。方法 选取我院在2016年12月至2018年12月期间收治的65例早期糖尿病肾病患者作为A组,同期65例糖尿病者作为B组,对两组患者行生化指标检验,比较两组指标变化。结果 A组各生化指标水平均高于对照组,CysC、RBP、mALB、TRF、β2-MG阳性检出率高于B组,有统计学意义(P<0.05),Cr检出率比较无差异(P>0.05)。结论 糖尿病肾病早期诊断中运用生化指标检验,有较高应用价值。

关键词： Local accuracy   Land cover change   Non-collocated   Reference sample data   Sampling   Class occurrence pattern indices   Cross-correlation   Pseudo cross-variograms  

摘要： Accuracy is an integral component in consumption of land cover and change information. Accuracy characterization in change categorization is more complicated than that in single-date classifications, especially when pursued at a local or per-pixel basis. In this paper, reference sample data consisting of reference class labels at sample locations are referred to as (model) training and validation sample data, respectively, when they are used for building and testing predictive models of local accuracy. They are preferably collocated to verify land cover and change types at the same locations across multiple single-date classifications. With collocated training data, methods devised for local accuracy mapping were usually implemented as direct extensions to those designed for accuracy predictions in single-date land cover maps, given that both response and explanatory variables are defined properly based on change maps. However, alternative methods are required when collocated training sample data are unavailable or sparse but non-collocated data collected at sample units not aligned across time exist or can be made available more conveniently and flexibly. This is typically the case for change-classification accuracy analyses, whereby prior sampling design and sample data may not remain informative in time but can be made adaptive by restructuring and augmentation to better represent strata of transitions as well as persistence. Such adaptability leads to great cost-saving in sampling than undertaking collocated sampling anew each time performing or revising accuracy mapping for a specific change analysis over a particular period. With a coherent review of related research, this paper proposes a geostatistical method for predictive mapping of local accuracies in any land-cover change maps created properly through adaptive use of complexly configured training sample data. This method works on a functional relation between accuracies in change-categorization and singl

关键词： Ischemic stroke   Stroke region segmentation   Feature extraction   Feature selection   Multi objective enhanced firefly algorithm (MO-EFFA)   Naive Bayes   Sampled-weighted random forests (SWRF)  

摘要： In Earth, the most crucial illness for cause of death is ischemic stroke. Ischemic stroke arises as a result of an obstacle within a blood vessel supplying blood to the brain. In this paper, for sub-acute ischemic stroke lesion segmentation, we utilize an effective meta-heuristic feature selection technique along with hybrid Naive Bayes (NB) and sample weighted random forest (SWRF) classification approach. Initially, the features are extracted from the pre-processed image, after that, the feature selection is done by using the multi-objective enhanced firefly algorithm. To improve the classification performance, the dimensionality of the feature vectors and errors are reduced by eliminating such irrelevant and redundant features. After the feature selection process, an ensemble of NB and SWRF classifiers is used for segmenting the image. Here the NB classifier is trained and applied to estimate the weights of training samples. Then, the training samples with estimated weights are utilized to train SWRF. In our work stroke lesion segmentation is formulated as a binary classification problem where every local region is classified as either affected or non-affected area.

关键词： influenza A virus   reassortment   packaging   zoonosis   antigenic shift  

摘要： Influenza A virus (IAV) has a segmented genome, which (i) allows for exchange of gene segments in coinfected cells, termed reassortment, and (ii) necessitates a selective packaging mechanism to ensure incorporation of a complete set of segments into virus particles. Packaging signals serve as segment identifiers and enable segment-specific packaging. We have previously shown that packaging signals limit reassortment between heterologous IAV strains in a segment-dependent manner. Here, we evaluated the extent to which packaging signals prevent reassortment events that would raise concern for pandemic emergence. Specifically, we tested the compatibility of hemagglutinin (HA) packaging signals from H5N8 and H7N9 avian IAVs with a human seasonal H3N2 IAV. By evaluating reassortment outcomes, we demonstrate that HA segments carrying H5 or H7 packaging signals are significantly disfavored for incorporation into a human H3N2 virus in both cell culture and a guinea pig model. However, incorporation of the heterologous HAs was not excluded fully, and variants with heterologous HA packaging signals were detected at low levels in vivo, including in naive contact animals. This work indicates that the likelihood of reassortment between human seasonal IAV and avian IAV is reduced by divergence in the RNA packaging signals of the HA segment. These findings offer important insight into the molecular mechanisms governing IAV emergence and inform efforts to estimate the risks posed by H7N9 and H5N8 subtype avian IAVs.

关键词： 聚合酶链反应,麻疹   病毒检验   临床价值  

摘要： 目的分析检验麻疹病毒采用PCR的临床效果。方法选取我中心2016年9月至2017年9月所检测的麻疹患者120例,根据患者采样时间分为试验组和参照组,每组各60例,参照组患者给予MV-IgM抗体检测,试验组患者给予聚合酶链反应检验,对比2组患者麻疹病毒阳性检出率,另外对患者发病后选取的样本进行检测,对患者阳性检出率进行记录。结果对患者发病7 d后采集的样本进行检测,试验组患者阳性反应50例,占总比83.33%,参照组患者阳性反应35例,占总比58.33%,试验组患者发病7 d后采集的样本检测阳性率83.33%高于参照组患者58.33%,组间对比差异性显著(P <0.05)。对比2组患者麻疹病毒阳性检出率对比,试验组患者阳性反应56例,参照组患者阳性反应45例,试验组患者麻疹病毒阳性检出率93.33%明显高于参照组阳性检出率75%,组间对比统计学意义存在(P<0.05)。结论对麻疹病毒检验采用聚合酶链反应检验,具有重要临床价值,对于初期患者来说,该种检验结果具有重要作用,临床价值较大。

关键词： 化妆品   容器造型   包装设计   品牌  

摘要： 随着科技水平的日新月异,人们的生活水平日渐提高,为了追求更优质的生活,化妆品及其包装设计成为人们装饰自己的必需品,这不仅仅是人类文明进步的标志,也是文化的重要载体之一。女性是化妆品的主要消费群体,化妆产品的包装一定要契合女性消费者的喜好,有针对性的进行设计,满足女性的对色彩、视觉等方面的需求,充分契合审美体验的要求。化妆品能够带给使用者自信、漂亮、青春的感受,是一种积极向上的生活方式。只有科学、合理的应用化妆品包装材料,把握化妆品的整体包装造型,才能够吸引消费者的眼光,使其产生购买欲望,促进化妆品的销售。