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Dimensionality reduction has a strong influence on image classification performance with the problems of strong coupling features and high band correlation. To solve these issues, we propose the Mahalanobis distance\u2013based kernel supervised machine learning framework for spectral dimensionality reduction. With Mahalanobis distance matrix\u2013based dimensional reduction, the coupling relationship between features and the elimination of the scale effect are removed in low-dimensional feature space, which benefits the image classification. The experimental results show that compared with other methods, the proposed algorithm demonstrates the best accuracy and efficiency. The Mahalanobis distance\u2013based multiples kernel learning achieves higher classification accuracy than the Euclidean distance kernel function. Accordingly, the proposed Mahalanobis distance\u2013based kernel supervised machine learning method performs well with respect to the spectral dimensionality reduction in hyperspectral imaging remote sensing. <\/jats:p>","DOI":"10.1177\/1550147720968467","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T07:47:53Z","timestamp":1606463273000},"page":"155014772096846","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Mahalanobis distance\u2013based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing"],"prefix":"10.1177","volume":"16","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin, 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