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Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data

Hong, Danfeng and Chanussot, Jocelyn and Yokoya, Naoto and Kang, Jian and Zhu, Xiao Xiang (2020) Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data. IEEE Geoscience and Remote Sensing Letters. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1545-598X (In Press)

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Abstract

Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention. However, most of these methods are designed by jointly learning feature representation from multi-modalities that exist in both training and test sets, yet they are less investigated in absence of certain modality in the test phase. To this end, in this letter, we propose to learn a shared feature space across multi-modalities in the training process. By this way, the out-of-sample from any of multi-modalities can be directly projected onto the learned space for a more effective cross-modality representation. More significantly, the shared space is regarded as a latent subspace in our proposed method, which connects the original multi-modal samples with label information to further improve the feature discrimination. Experiments are conducted on the multispectral-Lidar and hyperspectral dataset provided by the 2018 IEEE GRSS Data Fusion Contest to demonstrate the effectiveness and superiority of the proposed method in comparison with several popular baselines.

Item URL in elib:https://elib.dlr.de/129266/
Document Type:Article
Title:Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hong, Danfengdanfeng.hong (at) dlr.deUNSPECIFIED
Chanussot, Jocelynjocelyn (at) hi.isUNSPECIFIED
Yokoya, NaotoRIKENUNSPECIFIED
Kang, JiantumUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:In Press
Keywords:Cross-modality, feature learning, hyperspectral, multi-modality, multispectral-Lidar, shared subspace learning.
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: Hong, Danfeng
Deposited On:27 Sep 2019 11:27
Last Modified:05 Dec 2019 17:28

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