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The analysis and policymaking of national salt production require quality Salt Land maps with more detailed object information. This study successfully classified Salt Land into Active and Inactive based on pansharpening images using random forest algorithm. The results show: 1) the best pansharpening method is UNB PanSharp; 2) the texture variables have more significant contributions to enhance classification accuracy; 3) scheme 5 is the most efficient and recommended scheme in research, achieves an OA of 66.699% and a Kappa coefficient of 39.4%. This study can provide a good reference for developing Salt Land mapping and policies for practitioners or stakeholders. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]