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Domain Adversarial Neural Networks for Large-Scale Land Cover Classification

1
Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9, I-38123 Povo (TN), Italy
2
Athena Srl., Via Nenni, 7, I-37024 Negrar (VR), Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1153; https://doi.org/10.3390/rs11101153
Received: 29 March 2019 / Revised: 10 May 2019 / Accepted: 12 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Analysis of Big Data in Remote Sensing)
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Abstract

Learning classification models require sufficiently labeled training samples, however, collecting labeled samples for every new problem is time-consuming and costly. An alternative approach is to transfer knowledge from one problem to another, which is called transfer learning. Domain adaptation (DA) is a type of transfer learning that aims to find a new latent space where the domain discrepancy between the source and the target domain is negligible. In this work, we propose an unsupervised DA technique called domain adversarial neural networks (DANNs), composed of a feature extractor, a class predictor, and domain classifier blocks, for large-scale land cover classification. Contrary to the traditional methods that perform representation and classifier learning in separate stages, DANNs combine them into a single stage, thereby learning a new representation of the input data that is both domain-invariant and discriminative. Once trained, the classifier of a DANN can be used to predict both source and target domain labels. Additionally, we also modify the domain classifier of a DANN to evaluate its suitability for multi-target domain adaptation problems. Experimental results obtained for both single and multiple target DA problems show that the proposed method provides a performance gain of up to 40%. View Full-Text
Keywords: domain adaptation; domain adversarial neural networks; large-scale land cover classification; representation learning domain adaptation; domain adversarial neural networks; large-scale land cover classification; representation learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Bejiga, M.B.; Melgani, F.; Beraldini, P. Domain Adversarial Neural Networks for Large-Scale Land Cover Classification. Remote Sens. 2019, 11, 1153.

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