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However, the subspace learning methods based on LRR suffer from the problems of lacking robustness and discriminability. To address these issues, this paper proposes a new robust feature extraction method named the weighted Schatten p-norm minimization via low-rank discriminative embedding regression (WSNM-LRDER) method. This method works by integrating weighted Schatten p-norm and linear embedding regression into the LRR model. In WSNM-LRDER, the weighted Schatten p-norm is adopted to relax the low-rank function, which can discover the underlying structural information of the image, to enhance the robustness of projection learning. In order to improve the discriminability of the learned projection, an embedding regression regularization is constructed to make full use of prior information. The experimental results on three hyperspectral images datasets show that the proposed WSNM-LRDER achieves better performance than some advanced feature extraction methods. In particular, the proposed method yielded increases of more than 1.2%, 1.1%, and 2% in the overall accuracy (OA) for the Kennedy Space Center, Salinas, and Houston datasets, respectively, when comparing with the comparative methods.<\/jats:p>","DOI":"10.3390\/rs16163081","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"3081","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Low-Rank Discriminative Embedding Regression for Robust Feature Extraction of Hyperspectral Images via Weighted Schatten p-Norm Minimization"],"prefix":"10.3390","volume":"16","author":[{"given":"Chen-Feng","family":"Long","sequence":"first","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Ru","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2532-1567","authenticated-orcid":false,"given":"Yang-Jun","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China"},{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Ye","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xing-Hui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Engineering and Technology Research Center for Rural and Agricultural Informatization, Hunan Agricultural University, Changsha 410128, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Du","sequence":"additional","affiliation":[{"name":"College of Electrical and Computer Engineering, Mississippi State University, Mississippi, MS 39762, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101678","DOI":"10.1016\/j.ecoinf.2022.101678","article-title":"A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications","volume":"69","author":"Khan","year":"2022","journal-title":"Ecol. 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