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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Generalized Metaphor of Chinese Restaurant Franchise to Fusing Both Panchromatic and Multispectral Images for Unsupervised Classification
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A Generalized Metaphor of Chinese Restaurant Franchise to Fusing Both Panchromatic and Multispectral Images for Unsupervised Classification

机译：中国餐厅特许经营的全隐喻与全光谱和多光谱图像融合的无监督分类

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摘要

Two-step ways are often used for fusing both panchromatic (PAN) and multispectral (MS) images for classification, e.g., classifying MS images sharpened by PAN images or directly pouring fine spatial details of PAN images into a classification result of MS images. In this paper, we present a unified Bayesian framework to iteratively discovering semantic segments from PAN images and allocating cluster labels for the segments using MS images. Specifically, the probabilistic generative process of both PAN and MS images is explained with a generalized metaphor of the Chinese restaurant franchise (CRF) (gCRF), in which the two iterative random processes, i.e., table selection and dish selection, are adapted to discovering semantic segments in PAN images and inferring cluster labels for the discovered segments using MS images, respectively. Our major contributions are twofold: 1) The CRF is generalized into an image fusion framework by elegantly decomposing its two random processes, and 2) the random process of table selection in the CRF is transformed into stochastic image segmentation by enforcing spatial constraints over adjacent pixels. The qualitative analysis of experimental results shows that the gCRF can effectively utilize both the spatial details of the PAN images and the spectral information of the MS images. In terms of quantitative evaluation, the gCRF is comparable with support vector machine-based supervised classification methods.
机译：通常使用两步方式来融合全色（PAN）和多光谱（MS）图像以进行分类，例如，对通过PAN图像锐化的MS图像进行分类，或将PAN图像的精细空间细节直接注入到MS图像的分类结果中。在本文中，我们提出了一个统一的贝叶斯框架，以迭代地从PAN图像中发现语义段，并使用MS图像为这些段分配聚类标签。具体地说，用中餐馆特许经营权（CRF）（gCRF）的广义隐喻解释了PAN和MS图像的概率生成过程，其中两个迭代的随机过程，即餐桌选择和菜品选择，适合发现PAN图像中的语义片段，并分别使用MS图像为发现的片段推断聚类标签。我们的主要贡献有两个方面：1）通过优雅地分解其两个随机过程将CRF推广到图像融合框架中，以及2）通过对相邻像素执行空间约束，将CRF中表格选择的随机过程转换为随机图像分割。实验结果的定性分析表明，gCRF可以有效利用PAN图像的空间细节和MS图像的光谱信息。在定量评估方面，gCRF与基于支持向量机的监督分类方法相当。

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