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

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.