Markov random field (MRF) is currently the most common method to find the optimal solution for the classification of
image data incorporating contextual visual information. The labeling for a site in MRF is dependent on smoothing
parameters. Therefore, this paper deals with the development of a new robust two-step method to determine the
smoothing parameter which balances spatial and spectral energies for the purpose of maximizing the classification
accuracy. Multispectral images obtained by WorldView-2 satellite were employed in this research.
In the first step, a support vector machine (SVM) was used to provide a vector of multi-class probability and a class label
for each pixel. Then, the summation of the maximum probability of each pixel and its 8 neighbors is calculated for a
dynamic block and this value is assigned to the central pixels of each block. The blocks of each class are sorted and an
equal proportion of blocks of each class with the highest probability are selected. Then, the class codes and spectral
information of the selected blocks are extracted from the classified map and multispectral image, respectively. This
information is used to calculate class label co-occurrence matrices of the blocks (CLCMB), class label co-occurrence
matrix (CLCM) and class separability indices. Finally, different smoothing parameters are calculated and the results
show that estimated smoothing parameter can produce a more accurate map.