Perpustakaan Digital ITB

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 usingMS 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.