基于经验模态分解的高光谱图像分类决策融合

B. Demir, S. Erturk
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引用次数: 0

摘要

提出了一种基于经验模态分解(EMD)的决策融合方法来提高高光谱图像的分类精度。EMD是一种自适应信号分解方法,它将数据迭代分解为内禀模态函数(imf)。该方法首先将二维EMD应用于每个高光谱图像波段;然后,利用支持向量机(Support Vector Machine, SVM)对第一个IMF、第二个IMF、第一个IMF和第二个IMF和原始数据分别进行分类,并采用决策融合方法对得到的决策进行融合。实验结果表明,基于EMD的决策融合方法可以提高分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Empirical Mode Decomposition based decision fusion for hyperspectral image classification
This paper proposes an Empirical Mode Decomposition (EMD) based decision fusion approach to improve hyperspectral image classification accuracy. EMD is a adaptive signal decomposition method that iteratively decomposes the data into Intrinsic Mode Functions (IMFs). In the proposed approach, firstly two dimensional EMD is applied to each hyperspectral image band. Then, the first IMF, the second IMF, the sum of the first and second IMFs and the original data are individually classified using Support Vector Machine (SVM) and the obtained decisions are fused by a decision fusion approach. Experimental results demonstrate that the classification accuracy can be increased using the proposed EMD based decision fusion approach.
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