{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:12:05Z","timestamp":1771470725675,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T00:00:00Z","timestamp":1552003200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectral WorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of     81.34 %    ,     81.08 %     and     82.08 %     for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of     82 %    ,     80.82 %     and     77.96 %    . In Salinas, OAs are of     94.42 %    ,     95.83 %     and     94.16 %    . These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.<\/jats:p>","DOI":"10.3390\/rs11050575","type":"journal-article","created":{"date-parts":[[2019,3,8]],"date-time":"2019-03-08T11:21:59Z","timestamp":1552044119000},"page":"575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Evaluating the Performance of a Random Forest Kernel for Land Cover Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4269-164X","authenticated-orcid":false,"given":"Azar","family":"Zafari","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1769-6310","authenticated-orcid":false,"given":"Raul","family":"Zurita-Milla","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2179-1262","authenticated-orcid":false,"given":"Emma","family":"Izquierdo-Verdiguier","sequence":"additional","affiliation":[{"name":"Institute for Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Science (BOKU), A-1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3327","DOI":"10.1080\/01431160110104665","article-title":"Textural analysis of IRS-1D panchromatic data for land cover classification","volume":"23","author":"Rao","year":"2002","journal-title":"Int. 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