Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images

Abstract : A Markov random field is a graphical model that is commonly used to combine spectral information and spatial context into image classification problems. The contributions of the spatial versus spectral energies are typically defined by using a smoothing parameter, which is often set empirically. We propose a new framework to estimate the smoothing parameter. For this purpose, we introduce the new concepts of dynamic blocks and class label co-occurrence matrices. The estimation is then based on the analysis of the balance of spatial and spectral energies computed using the spatial class co-occurrence distribution and dynamic blocks. Moreover, we construct a new spatially weighted parameter to preserve the edges, based on the Canny edge detector. We evaluate the performance of the proposed method on three data sets: a multispectral DigitalGlobe WorldView-2 and two hyperspectral images, recorded by the AVIRIS and the ROSIS sensors, respectively. The experimental results show that the proposed method succeeds in estimating the optimal smoothing parameter and yields higher classification accuracies when compared to the state-of-the-art methods.
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Hossein Aghighi, John Trinder, Yuliya Tarabalka, Samsung Lim. Dynamic Block-Based Parameter Estimation for MRF Classification of High-Resolution Images. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2014, 11 (10), pp.1687-1691. ⟨hal-01016505⟩

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