Can we automatically choose best uncertainty heuristics for large margin active learning?

Active learning (AL) has shown a great potential in the field of remote sensing to improve the efficiency of the classification process while keeping a limited training dataset. Active learning uses heuristics to select the most informative pixels in each iteration. In literature, there are several metrics and selection criteria. In this paper, we focus on the uncertainty heuristics for large margin active learning. Existing uncertainty metrics are presented and combined to new ones using support vector machine learning algorithm. Besides, a new methodology is proposed, which automates a priori the choice of the best uncertainty heuristic. This contribution is evaluated on hyperspectral datasets while varying two parameters: class mixing and class balance. Finally discussion and conclusion are drawn.