A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

@article{Tuia2011ASO,
  title={A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification},
  author={Devis Tuia and Michele Volpi and Loris Copa and Mikhail F. Kanevski and Jordi Mu{\~n}oz-Mar{\'i}},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2011},
  volume={5},
  pages={606-617}
}
Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels… CONTINUE READING
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