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首页> 外文期刊>International journal of image and data fusion >Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier
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Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier

机译：使用中等分辨率的多光谱数据和神经模糊分类器在地中海环境中绘制燃烧区域图

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摘要

In this study, we assess the performance of a self-organising neuro-fuzzy classifier for burned area mapping using multi-spectral satellite data. The proposed neuro-fuzzy model incorporates a multi-layered structure consisting of two types of nodes. The first type is a generic fuzzy neuron classifier (FNCs), whereas the second is solely a decision fusion operator. The Group Method of Data Handling algorithm is used for structure learning providing the model with self-organising attributes and feature selection capabilities. The resulting novel structure consists not only of layers of FNCs but also of layers with only decision fusion due to the nature of the burned area mapping problem. The algorithm is applied to an entire LANDSAT-5 TM multi-spectral image, acquired over central Greece shortly after the major wildfire events of the summer of 2007. In addition to the self-organising neuro-fuzzy classifier, the image data set was classified using neural networks, support vector machines and AdaBoost algorithms. In general, the neuro-fuzzy burned area map presented the highest overall accuracy (more than 95%) compared to the other methods. However, the differences were not statistically significant as suggested by the results of the McNemar's test.
机译：在这项研究中，我们使用多光谱卫星数据评估自组织神经模糊分类器在燃烧区域映射中的性能。所提出的神经模糊模型结合了由两种类型的节点组成的多层结构。第一种是通用模糊神经元分类器（FNC），而第二种则只是决策融合算子。数据处理的分组方法算法用于结构学习，为模型提供自组织属性和特征选择功能。由于燃烧区域映射问题的性质，所得到的新颖结构不仅由FNC层组成，而且还由仅具有决策融合的层组成。该算法应用于2007年夏季重大野火事件发生后不久在希腊中部获得的整个LANDSAT-5 TM多光谱图像。除了自组织神经模糊分类器以外，还对图像数据集进行了分类使用神经网络，支持向量机和AdaBoost算法。通常，与其他方法相比，神经模糊灼伤区域图显示出最高的总体准确性（超过95％）。但是，如McNemar检验的结果所示，差异在统计学上并不显着。

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  • 来源
    《International journal of image and data fusion》 |2012年第4期|299-318|共20页
  • 作者

    Nikolaos E. Mitrakis; Giorgos Mallinis; Nikos Koutsias; John B. Theocharis;

  • 作者单位

    Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece,European Commission, Joint Research Centre, Institute for Protection and Security of the Citizen, Maritime Affairs Unit G.04, TP 051, 21027 Ispra (VA), Italy;

    School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Box 248, GR-54124, Thessaloniki, Greece;

    Department of Environmental and Natural Resources Management, University of Ioannina, 30100, Agrinio, Greece;

    Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece;

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  • 正文语种 eng
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  • 关键词

    neuro-fuzzy classification; decision fusion; fire scar mapping; feature selection; NN; SVM;

    机译：神经模糊分类;决策融合火痕图;特征选择;NN;支持向量机;

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