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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：通过无监督分类来表征地球的表面状态：以植被，水生和矿物表面为例

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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Dempster-Shafer理论（TDS）和Dezert-Smarandache理论（DSmT）的无监督分类方案，以表征植被，水生和矿物表面。从预处理的ASTER卫星图像（地理配准，几何校正和15 m重采样），通过确定光谱索引NDV1，MNDWI和NDBal（被视为用于给定像素分类的信息源）来生成新频道。 NDVI是一种对比功能，可以突出植被。因此，MNDWI可以表征水，而NDBal可以识别矿产资源。然后，我们分别对DST和DSmT的形式主义进行建模，这些形式主义是基于信念和融合函数的概念接近高级概率的建模工具，其中考虑了购置中遇到的某些缺陷（不确定性，无知等）。图片。另外，DST在DSmT期间管理源之间的分离形式化，同时管理源之间的分离和联合。接下来，我们实现了在MATLAB环境中实现的算法和相关代码。我们的贡献在于，通过使用基于简单高斯分布支持模型的质量函数来考虑与源信息相关的不完善之处（不准确性和不确定性），以便对每个焦点要素进行独立建模并评估一个焦点的归属。关于代表该类别的大多数元素的像素。结果表明，根据所有提出的模型，DST方法对于无监督的矿物表面和水生表面分类相对令人满意，而对于植被表面则不令人满意。对于DSmT，它对所有建议的模型都提供令人满意的结果。选择具有排除完整性约束E∩V∩M =Φ的模型作为最佳模型，因为除平均渐变像素的平均比率为93.34％之外，对地形的依从性比率为（96，37％）高于其他已实施模型。

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：无监督分类;DST;DSmT;ASTER卫星图像;NDVI;MNDWI;NDBal;PCR5;

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