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A mutual information-dempster-shafer based decision ensemble system for land cover classification of hyperspectral data

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Frontiers of Structural and Civil Engineering 2017年第4期11卷 774-783页

作者： Parham PAHLAVANI Behnaz BIGDELI Center of Excellence in Geomatics Engineering in Disaster Management School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran *** Iran

Hyperspectral images contain extremely rich spectral information that offer great potential to discrimi- nate between various land cover classes. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral classification. Furthermore, in the presence of mixed coverage pixels, crisp classifiers produced errors, omission and commission. This paper presents a mutual informa- tion-dempster-shafer system through an ensemble classi- fication approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to Then, a fuzzy maximum overcome high dimensionality. likelihood classifies each band subset. Finally, dempster-shafer is applied to fuse the results of the fuzzy classifiers. In order to assess the proposed method, a crisp ensemble system based on a support vector machine as the crisp classifier and weighted majority voting as the crisp fusion method are applied on hyperspectral data. Furthermore, a dimension reduction system is utilized to assess the effectiveness of mutual information band splitting of the proposed method. The proposed methodology provides interesting conclusions on the effectiveness and potentiality of mutual information- dempster-shafer based classification ofhyperspectral data.

关键词： mutual information dempster-shafer hyper- spectral classification support vector machine

Application of the dempster-shafer Theory to the Classification of Pixels from Aster Satellite Images and Spectral Indices

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Journal of Applied Mathematics and Physics 2017年第7期5卷 1462-1477页

作者： Jean-Claude Okaingni Sié Ouattara Adles Francis Kouassi Adama Koné Wognin Joseph Vangah Alain Clement Laboratory of Signals and Electrical Systems (L2SE)) Institut National Polytechnique Houphou&eumlt Boigny Yamoussoukro Cote D’Ivoire Institut National Polytechnique Houphouët Boigny (INPHB) Yamoussoukro Cote D’Ivoire Ecole Supérieure des Technologies de l’Information et de la Communication (ESATIC) Abidjan Cote D’Ivoire Institut Universitaire de Technologie d’Angers (IUT) Angers France

In this paper, it is proposed to apply the dempster-shafer Theory (DST) or the theory of evidence to map vegetation, aquatic and mineral surfaces with a view to detecting potential areas of observation of outcrops of geological formations (rocks, breastplates, regolith, etc.). The proposed approach consists in aggregating information by using the DST. From pretreated Aster satellite images (geo-referencing, geometric correction and resampling at 15 m), new channels were produced by determining the spectral indices NDVI, MNDWI and NDBaI. Then, the DST formalism was modeled and generated under the MATLAB software, an image segmented into six classes including three absolute classes (E,V,M) and three classes of confusion ({E,V}, {M,V}, {E,M}). The control on the land, based on geographic coordinates of pixels of different classes on said image, has made it possible to make a concordant interpretation thereof. Our contribution lies in taking into account imperfections (inaccuracies and uncertainties) related to source information by using mass functions based on a simple support model (two focal elements: the discernment framework and the potential set of belonging of the pixel to be classified) with a normal law for the good management of these.

关键词： dempster-shafer Theory Belief Functions Source Modeling Aster Satellite Images Spectral Indices Classification

基于dempster-shafer理论的大豆病害信息诊断模型

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农机化研究 2010年第8期32卷 50-52,78页

作者：王立舒东北农业大学工程学院哈尔滨150030

针对大豆生长病害智能诊断过程中计算机技术辅助决策的难点,构建了一个基于dempster-shafer理论的大豆病害信息诊断模型。对模型进行了理论及特性分析,建立了该种方法实现的理论框架,给出了具体的应用规则,并结合大豆病害的实例进行了... 详细信息

针对大豆生长病害智能诊断过程中计算机技术辅助决策的难点,构建了一个基于dempster-shafer理论的大豆病害信息诊断模型。对模型进行了理论及特性分析,建立了该种方法实现的理论框架,给出了具体的应用规则,并结合大豆病害的实例进行了具体应用。结果表明,该方法在大豆病害诊断过程中会大大提高决策的精度和可信度。

关键词：病害信息大豆 dempster-shafer 决策模型

基于dempster—shafer证据推理的信息融合

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舰船电子工程 2002年第5期22卷 12-17页

作者：夏佩伦海军潜艇学院青岛266071

深入介绍了dempster—shafer证据推理原理,以及它在信息融合应用中涉及的若干问题。为便于应用,还介绍了它的一种简化情况以及证据为随机变量的情况。作为对比,还介绍了该方法与贝叶斯推理相比的特点。

关键词： dempster-shafer 证据推理信息融合多传感器

基于dempster-shafer的飞行机器人多目标视觉定位方法

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计算机测量与控制 2019年第10期27卷 259-263页

作者：白璐杜承烈西北工业大学计算机学院

在受到遮挡物影响的室内环境中,飞行机器人接收数据中常伴有不确定性因素,为了解决复杂室内环境下高精准定位问题,提出了基于dempster-shafer的飞行机器人多目标视觉定位方法;根据飞行机器人控制原理,分析飞行位置与期望位置存在偏差,... 详细信息

在受到遮挡物影响的室内环境中,飞行机器人接收数据中常伴有不确定性因素,为了解决复杂室内环境下高精准定位问题,提出了基于dempster-shafer的飞行机器人多目标视觉定位方法;根据飞行机器人控制原理,分析飞行位置与期望位置存在偏差,通过提取颜色特征和边缘特征建立多目标模型;设计地面标记,采用迭代算法对标记地面目标进行局部最大化概率计算,以此适应多目标形变,通过dempster-shafer证据推理方法获取目标精准位置,由此完成多目标视觉定位;在实验场地支持下,将传统方法与dempster-shafer证据推理方法进行对比分析,由结果可知,dempster-shafer证据推理方法定位精准度最高可达到96%,对提高室内定位精准度具有一定价值。

关键词： dempster-shafer 飞行机器人多目标视觉定位

基于DS证据推理的信息安全风险评估方法研究

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计算机工程与应用 2009年第11期45卷 129-131页

作者：曹菊英赵跃龙湘南学院物理系湖南郴州423000 华南理工大学计算机科学与工程学院广州510640

在分析影响信息安全风险要素的基础上,构造了信息安全风险评估的层次结构模型,提出了基于dempster-shafer证据推理的信息安全风险评估方法。该方法可充分利用信息安全风险评估过程中专家的认知判断信息,处理评估过程中专家认知判断信息... 详细信息

在分析影响信息安全风险要素的基础上,构造了信息安全风险评估的层次结构模型,提出了基于dempster-shafer证据推理的信息安全风险评估方法。该方法可充分利用信息安全风险评估过程中专家的认知判断信息,处理评估过程中专家认知判断信息的不确定性问题,从而有效地提高风险评估的合理性。

关键词：信息安全风险 dempster-shafer 证据推理层次分析法

Dynamic Fault Diagnosis Using the Improved Linear Evidence Updating Strategy

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Journal of Shanghai Jiaotong university(Science) 2015年第4期20卷 427-436页

作者：尚群立张镇徐晓滨 College of Information Engineering Zhejiang University of Technology Institute of Systems Science and Control Engineering Hangzhou Dianzi University

The majority of the existing fault diagnosis methods using dempster-shafer(DS) evidence theory(DST) all provide the "static" fused results by combining several pieces of diagnosis evidence, which only reflect the current running status of monitored equipment. This paper presents the dynamic diagnosis strategy by using recursively the improved linear evidence updating rule. Its updated result can synthesize the diagnosis evidence collected at historical, current and future time steps by dynamically adjusting the proposed smoothing linear combination weights. The diagnosis examples of machine rotor show that the proposed method can provide more reliable and accurate results than the diagnosis methods based on the classical updating strategies.

关键词： dynamic fault diagnosis information fusion dempster-shafer evidence theory(DST) evidence updating

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D-S理论中证据损耗分析及改进方法

软件学报 2004年第1期15卷 69-75页

作者：徐凌宇张博锋徐炜民徐怀宇郭非凡上海大学计算机科学与工程学院上海200072 加州大学洛杉矶分校计算机系哈佛大学贝斯医学中心

分析了传统D-S理论在证据组合过程中的误差、抗干扰能力弱以及与人类思维相悖的原因;认为主要是由于部分定义和规则的局限性引发了组合过程中的证据损耗;提出了一种扩展的定义和组合规则,发现并解决了完全兼容证据组合过程中的证据损耗... 详细信息

分析了传统D-S理论在证据组合过程中的误差、抗干扰能力弱以及与人类思维相悖的原因;认为主要是由于部分定义和规则的局限性引发了组合过程中的证据损耗;提出了一种扩展的定义和组合规则,发现并解决了完全兼容证据组合过程中的证据损耗,有效地化解了冲突;理论分析和实验结果支持了方法的合理性与可行性.

关键词：信息融合 dempster-shafer 证据理论组合规则兼容冲突证据损耗

Estimation of Sensitivity of the DS/AHP Method While Solving Foresight Problems with Incomplete Data

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Intelligent Control and Automation 2013年第1期4卷 80-86页

作者： N. Pankratova N. Nedashkovskaya Institute for Applied Systems Analysis National Technical University of Ukraine “Kyiv Polytechnic Institute” Kyiv Ukraine

The paper provides mathematical analysis of sensitivity of different combination rules in the DS/AHP method when an alternative is added to the set of decision alternatives while solving foresight problems. Different cases of rank reversals are defined and two sets of conditions for these cases using the method DS/AHP are considered. Rank reversals are illustrated when the DS/AHP method is used to solve practical problem of critical technologies of energy conservation and power efficiency evaluation in Ukraine. It is shown that the DS/AHP method is not sensitive to exclusion (or addition) of an irrelevant decision alternative from (or to) the set of decision alternatives.

关键词： Analytic Hierarchy Process dempster-shafer Theory Incomplete Expert Information Multiple Criteria Analysis Combination Rules Rank Reversal Foresight Problems