Abstract

In this paper we discuss the issues of using inexact and inaccurate ground truth in the context of supervised learning. To leverage large amounts of Earth observation data for training algorithms, one often has to use ground truth which as not been carefully assessed. We address both the problems of training and evaluation. We first propose a weakly supervised approach for training change classifiers which is able to detect pixel-level changes in aerial images. We then propose a data poisoning approach to get a reliable estimate of the accuracy that can be expected from a classifier, even when the only ground-truth available does not match the reality. Both are assessed on practical land use and land cover applications.

Citation

@inproceedings{daudt2019learning,
  title={Learning to understand earth observation images with weak and unreliable ground truth},
  author={Daudt, Rodrigo Caye and Chan-Hon-Tong, Adrien and Le Saux, Bertrand and Boulch, Alexandre},
  booktitle={IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium},
  pages={5602--5605},
  year={2019},
  organization={IEEE}
}