Informative Priors for the Bayesian Classification of Satellite Images
该研究扩展了卫星图像贝叶斯分类中的先验分布，使其能建模道路、坡度等地形特征，并通过模拟和真实数据验证了该先验在重建算法中的有效性。
Abstract In the Bayesian classification of satellite images, a prior distribution is used that aims to model the belief of spatial homogeneity of the underlying region. We extend this prior distribution to model certain topographical features of the area such as the position of the roads, the slopes, and the aspects. We demonstrate the effectiveness of this prior distribution in a reconstruction algorithm by means of a simulation study in which the quality of the result is assessed by a comparison of estimated and known covertypes. We apply the algorithm to real data with success.