Land cover post-classifications by Markov chain geostatistical cosimulation based on pre-classifications by different conventional classifiers

ABSTRACT

The recently proposed Bayesian Markov chain random field (MCRF) cosimulation approach, as a new non-linear geostatistical cosimulation method, for land cover classification improvement (i.e. post-classification) may significantly increase classification accuracy by taking advantage of expert-interpreted data and pre-classified image data. The objective of this study is to explore the performance of the MCRF post-classification method based on pre-classification results from different conventional classifiers on a complex landscape. Five conventional classifiers, including maximum likelihood (ML), neural network (NN), Support Vector Machine (SVM), minimum distance (MD), and k-means (KM), were used to conduct land cover pre-classifications of a remotely sensed image with a 90,000 ha area and complex landscape. A sample dataset (0.32% of total pixels) was first interpreted based on expert knowledge from the image and other related data sources, and then MCRF cosimulations were performed conditionally on the expert-interpreted sample dataset and the five pre-classified image datasets, respectively. Finally, MCRF post-classification maps were compared with corresponding pre-classification maps. Results showed that the MCRF method achieved obvious accuracy improvements (ranging from 4.6% to 16.8%) in post-classifications compared to the pre-classification results from different pre-classifiers. This study indicates that the MCRF post-classification method is capable of improving land cover classification accuracy over different conventional classifiers by making use of multiple data sources (expert-interpreted data and pre-classified data) and spatial correlation information, even if the study area is relatively large and has a complex landscape.

Acknowledgements

We thank the editors and anonymous reviewers for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was supported in part by USA NSF [grant number 1414108].