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Unifying Top-down Views by Task-Specific Domain Adaptation

Lin, Jianzhe and Yu, Tianze and Mou, LiChao and Zhu, Xiao Xiang and Ward, Rabab Kreidieh and Wang, Z. Jane (2021) Unifying Top-down Views by Task-Specific Domain Adaptation. IEEE Transactions on Geoscience and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2020.3022608. ISSN 0196-2892. (In Press)

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Official URL: https://ieeexplore.ieee.org/document/9210589


In this article, we aim to learn a unified representation of images from satellite/aerial/ground views by exploring their underlying correlations. Inspired by recent advances in domain adaptation (DA), we propose a novel task-specific DA method for this purpose. Different from traditional DA methods, this proposed method not only applies task-specific classifiers1 but also introduces domain-specific tasks for different domains during the adaptation process. The experiments are conducted on two newly proposed ground-/satellite-to-aerial scene adaptation (GSSA) data sets. Since the semantic gap between the ground/satellite scenes and the aerial scenes is much larger than that between ground scenes, the DA task between these scenes is more challenging than traditional DA tasks. On GSSA data sets, we not only demonstrate the proposed unsupervised DA method but also explore the few-shot DA in the discussion section. The proposed method is easy to implement, and our method substantially outperforms the state-of-the-art methods on the studied data sets. We hope that the proposed method for the novel GSSA data sets can be a good baseline for future researchers. The related data sets/codes will be available online.

Item URL in elib:https://elib.dlr.de/140910/
Document Type:Article
Title:Unifying Top-down Views by Task-Specific Domain Adaptation
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Ward, Rabab KreidiehUBCUNSPECIFIED
Wang, Z. JaneTsinghua UniversityUNSPECIFIED
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2020.3022608
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Status:In Press
Keywords:Intelligent transportation systems-Unmanned vehicles, machine learning-predictive models
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Bratasanu, Ion-Dragos
Deposited On:12 Feb 2021 17:33
Last Modified:12 Feb 2021 17:33

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