{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:13:43Z","timestamp":1772043223287,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China;Tianjin Natural Science Foundation;Key Research and Development Project from Hebei Province","award":["No.U1813222;No.18JCYBJC16500;No.19210404D"],"award-info":[{"award-number":["No.U1813222;No.18JCYBJC16500;No.19210404D"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial\u2013spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial\u2013spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.<\/jats:p>","DOI":"10.3390\/s20164413","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T09:30:54Z","timestamp":1596792654000},"page":"4413","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial\u2013Spectral Weight Manifold Embedding"],"prefix":"10.3390","volume":"20","author":[{"given":"Hong","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"given":"Kewen","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"given":"Tiejun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"given":"Jie","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2796-5366","authenticated-orcid":false,"given":"Eunice","family":"Owoola","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gao, F., Wang, Q., and Dong, J. 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