ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 151–158, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-151-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 151–158, 2021
https://doi.org/10.5194/isprs-annals-V-3-2021-151-2021

  17 Jun 2021

17 Jun 2021

ADVERSARIAL DISCRIMINATIVE DOMAIN ADAPTATION FOR DEFORESTATION DETECTION

J. Noa1, P. J. Soto1, G. A. O. P. Costa2, D. Wittich3, R. Q. Feitosa1, and F. Rottensteiner3 J. Noa et al.
  • 1Dept. of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil
  • 2Dept. of Informatics and Computer Science, Rio de Janeiro State University (UERJ), Brazil
  • 3Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover (LUH), Germany

Keywords: change detection, deep learning, domain adaptation, deforestation, margin-based regularization

Abstract. Although very efficient in a number of application fields, deep learning based models are known to demand large amounts of labeled data for training. Particularly for remote sensing applications, responding to that demand is generally expensive and time consuming. Moreover, supervised training methods tend to perform poorly when they are tested with a set of samples that does not match the general characteristics of the training set. Domain adaptation methods can be used to mitigate those problems, especially in applications where labeled data is only available for a particular region or epoch, i.e., for a source domain, but not for a target domain on which the model should be tested. In this work we introduce a domain adaptation approach based on representation matching for the deforestation detection task. The approach follows the Adversarial Discriminative Domain Adaptation (ADDA) framework, and we introduce a margin-based regularization constraint in the learning process that promotes a better convergence of the model parameters during training. The approach is evaluated using three different domains, which represent sites in different forest biomes. The experimental results show that the approach is successful in the adaptation of most of the domain combination scenarios, usually with considerable gains in relation to the baselines.