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Activity Number: 254
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #305733
Title: Random Forests vs. Markov Random Fields for Land-Cover Classification
Author(s): Jason Stover*+
Companies: Georgia College
Address: Mathematics Department, Milledgeville, GA, 31061, United States
Keywords: iterated conditional modes ; Potts model ; kernel density estimates ; remote sensing ; MAP estimation

We present two approaches to classifying land cover: Random forests and Markov random fields. The data used to train each classifier consist of ground observations which appear on LANDSAT and ASTER satellite images taken over two years over central Georgia, U.S.A. Each type of classifier is then used to classify the remaining pixels from a central image, using reflectivities from it and thirty-seven others, along with georectified land-cover surveys from the U.S. Multi-Resolution Land Characteristics Consortium and Georgia Land Use Trends. Though the random forest classifier has a much lower misclassification rate, the sampling of the training data may make the random forest unable to detect important patterns that the Markov random field classifier can detect. This shortcoming of the random forest is not apparent from the estimated misclassification rate alone.

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