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This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.<\/jats:p>","DOI":"10.3390\/rs13061143","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T11:48:22Z","timestamp":1615981702000},"page":"1143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Yinghui","family":"Quan","sequence":"first","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingping","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1907-2664","authenticated-orcid":false,"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-6702","authenticated-orcid":false,"given":"Gabriel","family":"Dauphin","sequence":"additional","affiliation":[{"name":"Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, 93430 Paris, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1109\/LGRS.2014.2350263","article-title":"Generalized Graph-Based Fusion of Hyperspectral and LiDAR Data Using Morphological Features","volume":"12","author":"Liao","year":"2015","journal-title":"IEEE Geosci. 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