{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:07:09Z","timestamp":1753888029125,"version":"3.41.2"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":248,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Electronic and Optical Engineering, Shijiazhuang Campus, AEU"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression\u2010based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression\u2010based sparse unmixing via <jats:italic>K<\/jats:italic>\u2010means (DRSUM<jats:sub><jats:italic>K<\/jats:italic>\u2212means<\/jats:sub>) is proposed. The improved <jats:italic>K<\/jats:italic>\u2010means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using <jats:italic>l<\/jats:italic><jats:sub>2,0<\/jats:sub> norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row\u2010hard\u2010threshold function to solve the <jats:italic>l<\/jats:italic><jats:sub>2,0<\/jats:sub> norm directly. Then, DRSUM<jats:sub><jats:italic>K<\/jats:italic>\u2212means<\/jats:sub> can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSUM<jats:sub><jats:italic>K<\/jats:italic>\u2212means<\/jats:sub>.<\/jats:p>","DOI":"10.1155\/2021\/5575155","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T22:05:55Z","timestamp":1630965955000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Double Regression\u2010Based Sparse Unmixing for Hyperspectral Images"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1431-5762","authenticated-orcid":false,"given":"Shuaiyang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0875-2111","authenticated-orcid":false,"given":"Wenshen","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianghui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/79.974715"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/79.974718"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/8427459"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1081674"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2013.2279731"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/79.974727"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2012.2194696"},{"key":"e_1_2_10_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2010.2098413"},{"key":"e_1_2_10_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2013.2240001"},{"key":"e_1_2_10_10_2","first-page":"479","article-title":"Reweighted sparse regression for hyperspectral unmixing","volume":"4","author":"Zheng C. Y.","year":"2015","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_2_10_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2818703"},{"key":"e_1_2_10_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2191590"},{"key":"e_1_2_10_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2797200"},{"key":"e_1_2_10_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2943110"},{"key":"e_1_2_10_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2950700"},{"key":"e_1_2_10_16_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2018.1492175"},{"key":"e_1_2_10_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2551327"},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2020.1750733"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2020.3021520"},{"key":"e_1_2_10_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2873326"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11242897"},{"key":"e_1_2_10_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2002.1017616"},{"key":"e_1_2_10_23_2","doi-asserted-by":"publisher","DOI":"10.1137\/S0097539792240406"},{"key":"e_1_2_10_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2362056"},{"key":"e_1_2_10_25_2","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"e_1_2_10_26_2","doi-asserted-by":"crossref","unstructured":"ClarkR. G. SwayzeR. LivoK. E. WiseR. HoefenT. KokalyR. andSutleyS. J. USGS digital spectral library Splib06a: digital data series 231 September 2007 Available online:http:\/\/speclab.cr.usgs.gov\/spectral.lib06.","DOI":"10.3133\/ds231"},{"key":"e_1_2_10_27_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.14.046511"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2018.2878394"},{"key":"e_1_2_10_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2994260"},{"key":"e_1_2_10_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2016.2527782"}],"container-title":["Journal of Sensors"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5575155.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/js\/2021\/5575155.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/5575155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T01:24:35Z","timestamp":1722907475000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/5575155"}},"subtitle":[],"editor":[{"given":"Penghai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/5575155"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5575155","archive":["Portico"],"relation":{},"ISSN":["1687-725X","1687-7268"],"issn-type":[{"type":"print","value":"1687-725X"},{"type":"electronic","value":"1687-7268"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-02-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-09-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"5575155"}}