{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T09:23:03Z","timestamp":1768814583663,"version":"3.49.0"},"reference-count":98,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T00:00:00Z","timestamp":1553126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-2016-StG-714087"],"award-info":[{"award-number":["ERC-2016-StG-714087"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","award":["VH-NG-1018"],"award-info":[{"award-number":["VH-NG-1018"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2014the data domain or the manifold domain; and (2) how to make use of existing labeled data when formulating a graph to represent a manifold\u2014supervised, 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.<\/jats:p>","DOI":"10.3390\/rs11060681","type":"journal-article","created":{"date-parts":[[2019,3,21]],"date-time":"2019-03-21T12:28:01Z","timestamp":1553171281000},"page":"681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0690-5260","authenticated-orcid":false,"given":"Jingliang","family":"Hu","sequence":"first","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3212-9584","authenticated-orcid":false,"given":"Danfeng","family":"Hong","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany"}]},{"given":"Yuanyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany"}]},{"given":"Xiao Xiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. 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