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Multi-label Aerial Image Classification using A Bidirectional Class-wise Attention Network

Hua, Yuansheng and Mou, LiChao and Zhu, Xiao Xiang (2019) Multi-label Aerial Image Classification using A Bidirectional Class-wise Attention Network. In: 2019 Joint Urban Remote Sensing Event (JURSE), pp. 1-4. JURSE 2019, 22.-24. Mai 2019, Vannes, France. DOI: 10.1109/JURSE.2019.8808940

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Official URL: https://ieeexplore.ieee.org/document/8808940


Multi-label aerial image classification is of great significance in remote sensing community, and many researches have been conducted over the past few years. However, one common limitation shared by existing methods is that the co-occurrence relationship of various classes, so called class dependency, is underexplored and leads to an inconsiderate decision. In this paper, we propose a novel end-to-end network, namely class-wise attention-based convolutional and bidirectional LSTM network (CA-Conv-BiLSTM), for this task. The proposed network consists of three indispensable components: 1) a feature extraction module, 2) a class attention learning layer, and 3) a bidirectional LSTM-based sub-network. Experimental results on UCM multi-label dataset and DFC15 multi-label dataset validate the effectiveness of our model quantitatively and qualitatively.

Item URL in elib:https://elib.dlr.de/134068/
Document Type:Conference or Workshop Item (Speech)
Title:Multi-label Aerial Image Classification using A Bidirectional Class-wise Attention Network
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hua, YuanshengYuansheng.Hua (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:May 2019
Journal or Publication Title:2019 Joint Urban Remote Sensing Event (JURSE)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
DOI :10.1109/JURSE.2019.8808940
Page Range:pp. 1-4
EditorsEmailEditor's ORCID iD
Keywords:multi-label classification, high resolution aerial image, Convolutional Neural Network (CNN), class attention learning, Bidirectional Long Short-Term Memory (BiLSTM), class dependency
Event Title:JURSE 2019
Event Location:Vannes, France
Event Type:international Conference
Event Dates:22.-24. Mai 2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:11 Feb 2020 09:45
Last Modified:12 Feb 2020 09:56

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