Combining a random forest algorithm and a level set method for land cover mapping

In this paper, we introduce a new land cover mapping technique by taking advantages of a weighted random forest [1] and the level set method [2] to remove the weaknesses of each other. The weighted random forest can accurately estimate the likelihood that a pixel belonging to each classes while the level set method can capture the dependency among neighboring pixels. As a result, by combining their strengths, the resulting land cover map is more accurate as shown in our experiments for both simulated and actual datasets.