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首页> 外文会议>International Workshop on Intelligent Computing in Pattern Analysis/Synthesis(IWICPAS 2006); 20060826-27; Xi'an(CN) >Hierarchical Markovian Models for Hyperspectral Image Segmentation
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Hierarchical Markovian Models for Hyperspectral Image Segmentation

机译：高光谱图像分割的层次马尔可夫模型

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摘要

Hyperspectral images can be represented either as a set of images or as a set of spectra. Spectral classification and segmentation and data reduction are the main problems in hyperspectral image analysis. In this paper we propose a Bayesian estimation approach with an appropriate hiearchical model with hidden markovian variables which gives the possibility to jointly do data reduction, spectral classification and image segmentation. In the proposed model, the desired independent components are piecewise homogeneous images which share the same common hidden segmentation variable. Thus, the joint Bayesian estimation of this hidden variable as well as the sources and the mixing matrix of the source separation problem gives a solution for all the three problems of dimensionality reduction, spectra classification and segmentation of hyperspectral images. A few simulation results illustrate the performances of the proposed method compared to other classical methods usually used in hyperspectral image processing.
机译：高光谱图像可以表示为一组图像或一组光谱。光谱分类和分割以及数据约简是高光谱图像分析中的主要问题。在本文中，我们提出了一种贝叶斯估计方法，该方法具有适当的带有隐马尔可夫变量的分层模型，从而有可能共同进行数据约简，光谱分类和图像分割。在提出的模型中，所需的独立分量是分段的均质图像，它们共享相同的公共隐藏分段变量。因此，该隐藏变量以及源和分离源问题的混合矩阵的联合贝叶斯估计为三维降维，光谱分类和高光谱图像分割这三个问题提供了解决方案。一些仿真结果说明了该方法与通常用于高光谱图像处理的其他经典方法相比的性能。

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