{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T07:45:42Z","timestamp":1762674342234,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T00:00:00Z","timestamp":1566345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41601347"],"award-info":[{"award-number":["41601347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20160860"],"award-info":[{"award-number":["BK20160860"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2018B17814"],"award-info":[{"award-number":["2018B17814"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Found of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University","award":["17R04"],"award-info":[{"award-number":["17R04"]}]},{"name":"Open Research Fund in 2018 of Jiangsu Key Laboratory of Spectral Imaging &amp; Intelligent Sense","award":["3091801410406"],"award-info":[{"award-number":["3091801410406"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.<\/jats:p>","DOI":"10.3390\/rs11171959","type":"journal-article","created":{"date-parts":[[2019,8,21]],"date-time":"2019-08-21T11:19:06Z","timestamp":1566386346000},"page":"1959","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-2967","authenticated-orcid":false,"given":"Zhaohui","family":"Xue","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Sirui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"given":"Hongyan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430072, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430072, China"}]},{"given":"Peijun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/JPROC.2015.2462751","article-title":"Challenges and opportunities of multimodality and data fusion in remote sensing","volume":"103","author":"Mura","year":"2015","journal-title":"Proc. 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