A Linear Bayesian Updating Model for Probabilistic Spatial Classification
by
Xiang Huang and
Zhizhong Wang *
Xiang Huang and
Zhizhong Wang *
Department of Statistics, Central South University, Changsha 410012, Hunan, China
*
Author to whom correspondence should be addressed.
Academic Editor: Palmiro Poltronieri
Challenges 2016, 7(2), 21; https://doi.org/10.3390/challe7020021
Received: 8 September 2016 / Revised: 28 October 2016 / Accepted: 21 November 2016 / Published: 29 November 2016
Categorical variables are common in spatial data analysis. Traditional analytical methods for deriving probabilities of class occurrence, such as kriging-family algorithms, have been hindered by the discrete characteristics of categorical fields. To solve the challenge, this study introduces the theoretical backgrounds of the linear Bayesian updating (LBU) model for spatial classification through an expert system. The main purpose of this paper is to present the solid theoretical foundations of the LBU approach. Since the LBU idea is originated from aggregating expert opinions and is not restricted to conditional independent assumption (CIA), it may prove to be reasonably adequate for analyzing complex geospatial data sets, such as remote sensing images or area-class maps.
View Full-Text
Keywords:
expert opinions; linear Bayesian updating; spatial classification; transition probabilities
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Huang, X.; Wang, Z. A Linear Bayesian Updating Model for Probabilistic Spatial Classification. Challenges 2016, 7, 21.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
