Bayesian Markov Chain Random Field Cosimulation for Improving Land Cover Classification Accuracy

This study introduces a Bayesian Markov chain random field (MCRF) cosimulation approach for improving land-use/land-cover (LULC) classification accuracy through integrating expert-interpreted data and pre-classified image data. The expert-interpreted data are used as conditioning sample data in cosimulation, and may be interpreted from various sources. The pre-classification can be performed using any convenient conventional method. The approach uses the recently suggested MCRF cosimulation algorithm (Co-MCSS) to take a pre-classified image as auxiliary data while performing cosimulations conditioned on expert-interpreted data. It was tested using a series of expert-interpreted data sets and an image data set pre-classified by the supervised maximum likelihood (SML) algorithm. Results show that with the density W. Li (B) · C. Zhang Department of Geography, University of Connecticut, Storrs, CT 06269, USA e-mail: [email protected]; [email protected] C. Zhang · M. R. Willig · G. Wang Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA M. R. Willig Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA D. K. Dey Department of Statistics, University of Connecticut, Storrs, CT 06269, USA G. Wang Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA L. You International Food Policy Research Institute, Washington, DC 20006, USA