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A Topological Data Analysis Guided Fusion Algorithm: MAPPER-Regularized Manifold Alignment

Hu, Jingliang and Hong, Danfeng and Wang, Yuanyuan and Zhu, Xiao Xiang (2019) A Topological Data Analysis Guided Fusion Algorithm: MAPPER-Regularized Manifold Alignment. In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IGARSS 2019, 28.Juli - 02. Aug. 2019, Yokohama, Japan. DOI: 10.1109/IGARSS.2019.8898471

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Official URL: https://ieeexplore.ieee.org/abstract/document/8898471

Abstract

Hyperspectral images and polarimetric synthetic aperture radar (PolSAR) data are two important data sources, yet they barely appear under the same scope, even though multi-modal data fusion is attracting more and more attention. To our best knowledge, this paper investigates for the first time semi-supervised manifold alignment (SSMA) for the fusion of the hyperspectral image and PolSAR data. The SSMA searches a latent space where different data sources are aligned, which is accomplished by using the label information and the topological structure of the data. This paper is the first attempt to apply topological data analysis (TDA), a recent mathematic sub-field of data analysis, in remote sensing. It aims to reveal relevant information from the shape of a data in its feature space, and has been proven powerful in medicine. The paper also proposes a novel algorithm, MAPPER-regularized manifold alignment, which embeds the TDA into a semi-supervised manifold alignment for the fusion of the hyperspectral image and PolSAR data. The proposed algorithm exhibits superior performance in fusing a simulated EnMAP data set and a Sentinel-1 data set for an image of Berlin.

Item URL in elib:https://elib.dlr.de/128105/
Document Type:Conference or Workshop Item (Poster)
Title:A Topological Data Analysis Guided Fusion Algorithm: MAPPER-Regularized Manifold Alignment
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hu, Jingliangjingliang.hu (at) dlr.deUNSPECIFIED
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Wang, YuanyuanYuanyuan.Wang (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2019
Journal or Publication Title:2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI :10.1109/IGARSS.2019.8898471
Page Range:pp. 1-4
Status:Published
Keywords:Classification, data fusion, EnMAP, hyperspectral image, land cover, land use, manifold alignment, MAPPER, PolSAR, semi-supervised learning, Sentinel-1, topological data analysis (TDA)
Event Title:IGARSS 2019
Event Location:Yokohama, Japan
Event Type:international Conference
Event Dates:28.Juli - 02. Aug. 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: Hu, Jingliang
Deposited On:28 Jun 2019 10:48
Last Modified:06 Dec 2019 11:35

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