基于像斑的多光谱影像跨尺度分类研究

Application of Decision Tree on Multispectral Images Based on Segment and Scale-Span Features

  • 摘要: 提出了一种新的多尺度像斑模型,充分利用多尺度像斑模型所提供的尺度纵向信息,并结合决策树的分类方法来实现跨尺度分类,而不直接进行最佳尺度选择。实验结果证明,跨尺度方法较单一尺度分类能更准确地区分地物,从而提高分类精度。

     

    Abstract: Existing classification methods,based on the homogeneous-region,mostly involve the best segmentation criterion choice.Using the so-called best-scale to classify the multi-scale objects defined by human subjectivity,the paper doesn't think it is the best way for classification.So the paper proposes a new multi-scale homogeneous-region model,fully using the longitudinal information which the homogeneous-region model provides,and adopting the scale-span classification method based on decision tree to improve the accuracy,without selecting the best-scale data.The result shows this method can distinguish objects accurately and improve the precision than sole scale classification.

     

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