Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm

This paper describes an approach to using the Random Forest classification algorithm to quantitatively evaluate a range of potential image segmentation scale alternatives in order to identify the segmentation scale(s) that best predict land cover classes of interest. The image segmentation scale selection process was used to identify three critical image object scales that when combined produced an optimal level of land cover classification accuracy. Following segmentation scale optimization, the Random Forest classifier was then used to assign land cover classes to 11 scenes of SPOT satellite imagery in North and South Dakota with an average overall accuracy of 85.2 percent.