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首页> 外文期刊>American journal of applied sciences >Characterization of the Earth's Surface State by Unsupervised Classification: Case of Vegetated, Aquatic and Mineral Surfaces
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Characterization of the Earth's Surface State by Unsupervised Classification: Case of Vegetated, Aquatic and Mineral Surfaces

机译：无监督分类表征地球表面状态：植被，水生和矿物表面的情况

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摘要

In this study, we propose an unsupervised classification scheme based on the Dempster-Shafer Theory (TDS) and the Dezert-Smarandache Theory (DSmT) to characterize vegetated, aquatic and mineral surfaces. From pre-processed ASTER satellite images (georeferencing, geometric correction and 15 m re-sampling), neo-channels were produced by determining the spectral indices NDVl, MNDWI and NDBal, considered as sources of information for classification of a given pixel. NDVI is a contrast function to highlight vegetation. By account, the MNDWI makes it possible to characterize the water and the NDBal makes it possible to recognize the mineral resources. Then, we modeled respectively the formalisms of the DST and the DSmT, these formalisms are modeling tools close to advanced probabilities based on the notions of belief and fusion functions to take into account certain imperfections (uncertainty, ignorance, etc.) encountered in the acquisition of images. In addition, the DST manages a formalism of disjunction between the sources during the DSmT simultaneously manages a disjunction and a conjunction between the sources. Next we realized the algorithms and related codes that we implemented in the MATLAB environment. Our contribution lies in taking into account the imperfections (inaccuracies and uncertainties) linked to source information through the use of mass functions based on a simple Gaussian distribution support model in order to model each focal element independently of the others and to evaluate the belonging of a pixel to a class with respect to the majority of elements representing said class. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models. As for the DSmT, it presents satisfactory results for all the models proposed. The model with the exclusion integrity constraint E∩V ∩ M = Φ was selected as the best model because having, in addition to an average rate of well-graded pixels of 93.34%, a compliance rate (96, 37%) with the terrain higher than those of the other models implemented.
机译：在这项研究中，我们提出了一种基于Deppster-Shafer理论（TDS）和Dezert-Smarandache理论（DSMT）的无监督的分类方案，以表征植被，水生和矿物表面。从预处理的紫色卫星图像（地理传播，几何校正和15M重新采样），通过确定光谱指数NDVL，MNDWI和NDBAL来产生新通道，被认为是用于对给定像素分类的信息来源。 NDVI是突出植被的对比功能。通过账户，MNDWI使得可以表征水和NDBAL使得可以识别矿产资源。然后，我们分别模拟了DST和DSMT的形式主义，这些形式主义是根据信仰和融合功能的概念接近先进概率的建模工具，以考虑在收购中遇到的某些缺陷（不确定性，无知等）图像。此外，DST管理在DSMT期间，在DSMT期间，在DSMT期间同时管理源之间的差异和源之间的结合。接下来我们实现了我们在Matlab环境中实现的算法和相关代码。我们的贡献在考虑到通过使用基于简单的高斯分布支持模型的质量功能与源信息相关的缺陷（不准确性和不确定性），以便独立于其他焦点模型，并评估A的归属关于代表所述类的大多数元素的类别的像素。结果结果表明，根据所有提出的模型，DST方法对于无矿物表面和水生表面的矿物表面和水生表面的分类相对令人满意。至于DSMT，它为所有提议的模型提供了令人满意的结果。具有排除完整性约束e∩v∩m=φ的模型被选为最佳模型，因为除了93.34％的良好分级像素的平均速率之外，符合性率（96,37％）与地形高于实施的其他模型。

著录项

  • 来源
    《American journal of applied sciences》 |2018年第7期|358-369|共12页
  • 作者

    Jean-Claude Okaingni; Sié Ouattara; Adles Kouassi; Alain Clément;

  • 作者单位

    Laboratory of Signals and Electrical Systems (L2SE)) Institut National Polytechnique Houphouët Boigny Yamoussoukro Cote D 'Ivoire;

    Laboratory of Signals and Electrical Systems (L2SE)) Institut National Polytechnique Houphouët Boigny Yamoussoukro Cote D 'Ivoire;

    Laboratory of Signals and Electrical Systems (L2SE)) Institut National Polytechnique Houphouët Boigny Yamoussoukro Cote D 'Ivoire Ecole Supérieure des Technologies de l 'Information et de la Communication (ESA TIC) Abidjan Cote d 'Ivoire;

    Institut Universitaire de Technologie d'Angers (IUT) Angers France;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Unsupervised Classification; DST; DSmT; ASTER Satellite Images; NDVI; MNDWI; NDBal; PCR5;

    机译：无监督的分类;DST;DSMT;艾斯特卫星图像;ndvi;MNDWI;ndbal;PCR5.;

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