 Hyperparameter estimation and plug-in kernel density estimates for maximum a posteriori land-cover classification with multiband satellite dataJason H. Stover and Matthew C. Ulm Computational Statistics & Data Analysis, 2013, vol. 57, issue 1, 82-94 Abstract: Classifying land cover via satellite imagery is an important problem in geographical studies. This paper presents a maximum a posteriori (MAP) land-cover classifier for multiband satellite data. The method uses the Markov random field model. The MAP estimation is carried out via iterated conditional modes. The reflectivities are transformed via principal components to overcome correlation and skewness, and then the distributions of the components are estimated with kernel densities. To make use of a large amount of past work in the area, the prior distribution is selected from the US Geological Survey’s land-cover database. Prior hyperparameters are estimated by equating some of their differences with the logarithms of ratios of relative frequencies from the land-cover survey, and then estimated via least squares and a bagging-type procedure. The resulting classifier produces a smooth image and captures detailed features of the study area, including roads. Keywords: Iterated conditional modes; Naive Bayes; Remote sensing (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:57:y:2013:i:1:p:82-94 DOI: 10.1016/j.csda.2012.06.010 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.