高光谱图像分割:蝴蝶方法

N. Gorretta, J. Roger, G. Rabatel, V. Bellon-Maurel, C. Fiorio, C. Lelong
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引用次数: 17

摘要

文献中很少提出将高光谱图像的光谱和空间维度进行耦合的方法。本文提出了一种基于迭代过程和光谱信息与空间信息交叉分析的通用分割方案——蝴蝶分割方案。实际上,空间和空间结构分别在空间和光谱空间中提取,两者都考虑到另一个。为了将这种布局应用于高光谱图像,我们特别关注空间和光谱结构,即空间和光谱空间的拓扑概念和潜在变量。并提出了与这些结构的合作方案。最后,给出并讨论了蝴蝶方法在真实高光谱图像上的具体实现结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hyperspectral image segmentation: The butterfly approach
Few methods are proposed in the litterature for coupling the spectral and the spatial dimension available on hyperspectral images. This paper proposes a generic segmentation scheme named butterfly based on an iterative process and a cross analysis of spectral and spatial information. Indeed, spatial and spatial structures are extracted in spatial and spectral space respectively both taking into account the other one. To apply this layout on hyperspectral imgages, we focus particulary on spatial and spectral structures i.e. topologic concepts and latent variable for the spatial and the spectral space respectively. Moreover, a cooperation scheme with these structures is proposed. Finally, results obtained on real hyperspectral images using this specific implementation of the butterfly approach are presented and discussed.
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