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首页> 外文期刊>Frontiers of earth science >A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data
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A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data

机译：基于互信息-Dempster-Shafer的决策集合系统，用于高光谱数据的土地覆盖分类

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摘要

Hyperspectral images contain extremely rich spectral information that offer great potential to discriminate 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 information-Dempster-Shafer system through an ensemble classification approach for classification of hyperspectral data. First, mutual information is applied to split data into a few independent partitions to overcome high dimensionality. Then, a fuzzy maximum 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 of hyperspectral data.
机译：高光谱图像包含极其丰富的光谱信息，这为区分各种土地覆盖类别提供了巨​​大的潜力。但是，这些图像通常由几十个或几百个光谱接近的波段组成，这导致高光谱分类中的高冗余度和大量的计算时间。此外，在覆盖像素混合的情况下，清晰的分类器会产生错误，遗漏和委托。本文通过集成分类方法提出了一种用于高光谱数据分类的互信息-Dempster-Shafer系统。首先，使用互信息将数据分成几个独立的分区以克服高维性。然后，模糊最大似然将每个波段子集分类。最后，应用Dempster-Shafer融合模糊分类器的结果。为了评估该方法，在高光谱数据上应用了基于支持向量机作为脆性分类器和加权多数投票作为脆性融合方法的脆性集成系统。此外，使用降维系统来评估所提出方法的相互信息频带划分的有效性。所提出的方法为基于互信息-Dempster-Shafer的高光谱数据分类的有效性和潜力提供了有趣的结论。

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