{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:44:25Z","timestamp":1776329065187,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,23]],"date-time":"2019-05-23T00:00:00Z","timestamp":1558569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006250","name":"Southwest University","doi-asserted-by":"publisher","award":["XDJK2019B008"],"award-info":[{"award-number":["XDJK2019B008"]}],"id":[{"id":"10.13039\/501100006250","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA\u2019s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%\u20130.74%, 3.88%\u20134.37%, and 0.39%\u20134.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%\u20132.68%, 0.12%\u20131.10%, and 0.01%\u20130.08%, respectively, when compared with the method based only on the most suitable fundamental image.<\/jats:p>","DOI":"10.3390\/rs11101219","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T02:22:00Z","timestamp":1558664520000},"page":"1219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1774-8248","authenticated-orcid":false,"given":"Lan","family":"Zhang","sequence":"first","affiliation":[{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geography Science, Southwest University, Chongqing 400715, China"},{"name":"State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China"}]},{"given":"Hongjun","family":"Su","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4318-8405","authenticated-orcid":false,"given":"Jingwei","family":"Shen","sequence":"additional","affiliation":[{"name":"Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geography Science, Southwest University, Chongqing 400715, China"},{"name":"State Cultivation Base of Eco-agriculture for Southwest Mountainous Land, Southwest University, Chongqing 400715, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/MGRS.2018.2793873","article-title":"Discriminant analysis-based dimension reduction for hyperspectral image classification: A survey of the most recent advances and an experimental comparison of different techniques","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Geosci. 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