{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:04:34Z","timestamp":1783577074974,"version":"3.55.0"},"reference-count":92,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T00:00:00Z","timestamp":1540252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration\u2019s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications\u2019 underlying efficacies.<\/jats:p>","DOI":"10.3390\/rs10111675","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T02:59:40Z","timestamp":1540349980000},"page":"1675","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation"],"prefix":"10.3390","volume":"10","author":[{"given":"Devin","family":"Routh","sequence":"first","affiliation":[{"name":"Department of Environmental Systems Science, ETH Z\u00fcrich, 8092 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lindsi","family":"Seegmiller","sequence":"additional","affiliation":[{"name":"Division of Geoinformatics, Department of Urban Planning and Environment, KTH Royal Institute of Technology, SE-114 28 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlie","family":"Bettigole","sequence":"additional","affiliation":[{"name":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Kuhn","sequence":"additional","affiliation":[{"name":"School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chadwick D.","family":"Oliver","sequence":"additional","affiliation":[{"name":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2956-530X","authenticated-orcid":false,"given":"Henry B.","family":"Glick","sequence":"additional","affiliation":[{"name":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4138","DOI":"10.1109\/TGRS.2011.2161585","article-title":"Analysis of imaging spectrometer data using N-dimensional geometry and a mixture-tuned matched filtering approach","volume":"49","author":"Boardman","year":"2011","journal-title":"IEEE Trans. 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