Putting the user into the active learning loop: towards realistic but efficient photointerpretation

In recent years, several studies have been published about the smart definition of training set using active learning algorithms. However, none of these works consider the contradiction between the active learning methods, which rank the pixels according to their uncertainty, and the confidence of the user in labeling, which is related both to the homogeneity of the pixel context and to the knowledge of the user of the scene. In this paper, we propose a two-steps procedure based on a filtering scheme to learn the confidence of the user in labeling. This way, candidate training pixels are ranked according both to their uncertainty and to the chances of being labeled correctly by the user. In this way, we avoid the queries where the user would not be able to provide a class for the pixel. We consider the capacity of a model in learning the user's confidence and report experiments on a QuickBird image: the filtering scheme proposed maximizes the number of useful queries with respect to traditional active learning.

Published in:
2012 Ieee International Geoscience And Remote Sensing Symposium (Igarss), 75-78
Presented at:
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012, Munich, July 22-27
New York, Ieee

 Record created 2012-07-06, last modified 2018-03-13

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