{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:00:27Z","timestamp":1776412827588,"version":"3.51.2"},"reference-count":96,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council (ERC)","doi-asserted-by":"publisher","award":["755617"],"award-info":[{"award-number":["755617"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ram\u00f3n y Cajal Contract (Spanish Ministry of Science, Innovation and Universities)","award":["755617"],"award-info":[{"award-number":["755617"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO\u2019s software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land\u2013grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO\u2019s MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.<\/jats:p>","DOI":"10.3390\/rs14184452","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4452","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Introducing ARTMO\u2019s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape"],"prefix":"10.3390","volume":"14","author":[{"given":"Masoumeh","family":"Aghababaei","sequence":"first","affiliation":[{"name":"Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5166-4646","authenticated-orcid":false,"given":"Ataollah","family":"Ebrahimi","sequence":"additional","affiliation":[{"name":"Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6239-7670","authenticated-orcid":false,"given":"Ali Asghar","family":"Naghipour","sequence":"additional","affiliation":[{"name":"Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran"}]},{"given":"Esmaeil","family":"Asadi","sequence":"additional","affiliation":[{"name":"Department of Range and Watershed Management, Faculty of Natural Resources and Earth Sciences, Shahrekord University, Shahrekord 8818634141, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-4454","authenticated-orcid":false,"given":"Adri\u00e1n","family":"P\u00e9rez-Suay","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, Paterna, 46980 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0537-6803","authenticated-orcid":false,"given":"Miguel","family":"Morata","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, Paterna, 46980 Valencia, Spain"}]},{"given":"Jose Luis","family":"Garcia","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, Paterna, 46980 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3188-1448","authenticated-orcid":false,"given":"Juan Pablo","family":"Rivera Caicedo","sequence":"additional","affiliation":[{"name":"Secretary of Research and Graduate Studies, CONACYT-UAN, Tepic 63155, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6313-2081","authenticated-orcid":false,"given":"Jochem","family":"Verrelst","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory (IPL), University of Valencia, C\/Catedr\u00e1tico Jos\u00e9 Beltr\u00e1n 2, Paterna, 46980 Valencia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"key":"ref_1","first-page":"83","article-title":"A SPECLib-based operational classification approach: A preliminary test on China land cover mapping at 30 m","volume":"71","author":"Zhang","year":"2018","journal-title":"Int. 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