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A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion

Hu, Jingliang and Hong, Danfeng and Wang, Yuanyuan and Zhu, Xiao Xiang (2019) A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion. Remote Sensing, 11 (6), pp. 1-28. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs11060681 ISSN 2072-4292

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Official URL: https://www.mdpi.com/2072-4292/11/6/681

Abstract

In remote sensing, hyperspectral and polarimetric synthetic aperture radar (PolSAR) images are the two most versatile data sources for a wide range of applications such as land use land cover classification. However, the fusion of these two data sources receive less attention than many other, because of their scarce data availability, and relatively challenging fusion task caused by their distinct imaging geometries. Among the existing fusion methods, including manifold learning-based, kernel-based, ensemble-based, and matrix factorization, manifold learning is one of most celebrated techniques for the fusion of heterogeneous data. Therefore, this paper aims to promote the research in hyperspectral and PolSAR data fusion, by providing a comprehensive comparison between existing manifold learning-based fusion algorithms. We conducted experiments on 16 state-of-the-art manifold learning algorithms that embrace two important research questions in manifold learning-based fusion of hyperspectral and PolSAR data: (1) in which domain should the data be aligned---the data domain or the manifold domain; and (2) how to make use of existing labeled data when formulating a graph to represent a manifold---supervised, semi-supervised, or unsupervised. The performance of the algorithms were evaluated via multiple accuracy metrics of land use land cover classification over two data sets. Results show that the algorithms based on manifold alignment generally outperform those based on data alignment (data concatenation). Semi-supervised manifold alignment fusion algorithms performs the best among all. Experiments using multiple classifiers show that they outperform the benchmark data alignment-based algorithms by ca. 3\% in terms of the overall classification accuracy.

Item URL in elib:https://elib.dlr.de/128102/
Document Type:Article
Title:A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion
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:March 2019
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:11
DOI :10.3390/rs11060681
Page Range:pp. 1-28
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
ISSN:2072-4292
Status:Published
Keywords:data fusion; generalized graph fusion; hyperspectral image; data alignment; locality preserving projections; manifold alignment; manifold learning; MAPPER-induced manifold alignment; polarimetric SAR; manifold alignment; MIMA
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 10:37
Last Modified:14 Dec 2019 04:27

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