Abstract:
Due to the increasing amount of remotely sensed data, methods for its automatic interpretation become more and more important. Corresponding supervised learning technique...Show MoreMetadata
Abstract:
Due to the increasing amount of remotely sensed data, methods for its automatic interpretation become more and more important. Corresponding supervised learning techniques, however, strongly depend on the availability of training data, i.e. data where measurements and labels are provided simultaneously. The creation of reference data for large data sets is very challenging and approaches addressing this task often introduce a significant amount of label noise. While other works focused on the influence of label noise on the training process, this paper studies the impact on the evaluation and shows that the corresponding effects are even more adverse.
Date of Conference: 28 July 2019 - 02 August 2019
Date Added to IEEE Xplore: 14 November 2019
ISBN Information: