Informative Priors for the Bayesian Classification of Satellite Images
作者:
Arnoldo Frigessi,
Julian Stander,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1994)
卷期:
Volume 89,
issue 426
页码: 703-709
ISSN:0162-1459
年代: 1994
DOI:10.1080/01621459.1994.10476797
出版商: Taylor & Francis Group
关键词: Geographical Information System;ICM algorithm;Image reconstruction;Markov random fields;Quality assessment;Remote sensing
数据来源: Taylor
摘要:
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.
点击下载:
PDF (1231KB)
返 回