IMPROVING LAND COVER CLASSIFICATION IN SUBARCTIC WETLANDS USING MULTI-SOURCE REMOTELY SENSED DATA

A decision-level fusion method was developed to classify eleven cover types in a sub-arctic ecosystem, including intertidal marsh, tundra heath, peat plateau, open fen, shrub-rich fen, wet fen, conifer swamp, shrub swamp, conifer, and lichen woodland using Sentinel-1 and Sentinel-2 data. An index was also designed to measure the classification uncertainty for individual pixels. Three classifiers based on Random Forest (RF) classification were first designed and carried out, and the classification results were then combined within the framework of Dempster-Shaffer (DS) theory. The developed method increased the overall accuracy by 2.5% based on the test samples and reduced the percentage of pixels with high uncertainty by 6.4%, compared with the feature-level fusion method.