{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T21:38:27Z","timestamp":1772833107031,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T00:00:00Z","timestamp":1642118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.<\/jats:p>","DOI":"10.3390\/rs14020383","type":"journal-article","created":{"date-parts":[[2022,1,16]],"date-time":"2022-01-16T20:45:21Z","timestamp":1642365921000},"page":"383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing"],"prefix":"10.3390","volume":"14","author":[{"given":"Xinxi","family":"Feng","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Yango University, Fuzhou 350015, China"}]},{"given":"Le","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer Science and Techology, Zhejiang University, Hangzhou 310007, China"}]},{"given":"Le","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7894","DOI":"10.1109\/TGRS.2019.2917161","article-title":"A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. 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