Semisupervised adversarial discriminative domain adaptation, with application to remote sensing data

Published

Conference Paper

© 2018 IEEE Recently, convolutional neural networks (CNNs) have received substantial attention in the literature for object recognition (e.g., buildings and roads) in several remote sensing data modalities (e.g., aerial color imagery). Although CNNs have exhibited excellent recognition performance, recent research suggests that trained CNNs can often perform very poorly when applied to data collected over new geographic regions, and for which little labeled training data is available. In this work, we consider the adversarial discriminative domain adaptation (ADDA) approach to address this limitation, due its recent success on related problems. A limitation of ADDA is that it is unsupervised, so in this work we extend ADDA to a semi-supervised algorithm, in which we assume that both labeled and unlabeled data are available in the new domain (e.g., in new geographic region to be evaluated). We compare semisupervised ADDA to ADDA and a standard fine-tuning approach wherein available labeled data is used for standard CNN training. We perform experiments on two remote sensing datasets and the results indicate that semi-supervised ADDA consistently improves over the other approaches when small amounts of labeled training data are available in the new domain.

Full Text

Duke Authors

Cited Authors

  • Wang, R; Collins, LM; Bradbury, K; Malof, JM

Published Date

  • October 31, 2018

Published In

  • International Geoscience and Remote Sensing Symposium (Igarss)

Volume / Issue

  • 2018-July /

Start / End Page

  • 3611 - 3614

International Standard Book Number 13 (ISBN-13)

  • 9781538671504

Digital Object Identifier (DOI)

  • 10.1109/IGARSS.2018.8518096

Citation Source

  • Scopus