Machine learning methods for sub-pixel land-cover classification in the spatially heterogeneous region of Flanders (Belgium): a multi-criteria comparison

摘要

Until now, few research has addressed the use of machine learning methods for classification at the sub-pixel level. To close this knowledge gap, in this article, six machine learning methods were compared for the specific task of sub-pixel land-cover extraction in the spatially heterogeneous region of Flanders (Belgium). In addition to the classification accuracy at the pixel and the municipality level, three evaluation criteria reflecting the methods' ease-of-use were added to the comparison: the time needed for training, the number of meta-parameters, and the minimum training set size. Robustness to changing training data was also included as the sixth evaluation criterion. Based on their scores for these six criteria, the machine learning methods were ranked according to three multi-criteria ranking scenarios. These ranking scenarios correspond to different decision-making scenarios that differ in their weighting of the criteria. In general, no overall winner could be designated: no method performs best for all evaluation scenarios. However, when both time available for preprocessing and the magnitude of the training data set are unconstrained, Support Vector Machines (SVMs) clearly outperform the other methods.