30 December 1994 Classification of remote sensing images with the aid of Gibbs distribution
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In a maximum likelihood classification (MLC) of a satellite image it is explicitly assumed that the spectral properties of one pixel are independent of the properties of all other pixels. As a result, the MLC is unable to distinguish the pixels which come from different land-cover classes but have the same spectral properties and the result is usually a snow-like map. On the other hand, data in the field of remote sensing often appear in the form of distinct parcels and all the pixels in a specific parcel are assumed to come from a single land-cover class. Therefore, there must exist some spatial continuity between adjacent pixels. This property must be of great importance and should be taken into account in the process of land-cover classification. This paper proposes to make use of the theory of the Markov random field (MRF) and the Gibbs distribution for imposing the spatial continuity either by making use of the joint distribution of the Gibbs distribution and the conventional multinormal distribution or by using the Gibbs distribution separately as a postprocessing procedure to the MLC. While the application of the joint distribution and the Gibbs distribution may result in different classifications, experiments show that very significant improvement can be achieved with at least one of these models.
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D. Zhang, D. Zhang, Luc J. Van Gool, Luc J. Van Gool, Andre J. Oosterlinck, Andre J. Oosterlinck, } "Classification of remote sensing images with the aid of Gibbs distribution", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196746; https://doi.org/10.1117/12.196746

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