{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T20:23:51Z","timestamp":1774556631571,"version":"3.50.1"},"reference-count":80,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2015,10,30]],"date-time":"2015-10-30T00:00:00Z","timestamp":1446163200000},"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>Only well-chosen segmentation parameters ensure optimum results of  object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A  sub-tropical study site located in S\u00e3o Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 \u00d7 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n \u2248 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a  by-product to the classification map.<\/jats:p>","DOI":"10.3390\/rs71114482","type":"journal-article","created":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T02:53:57Z","timestamp":1446432837000},"page":"14482-14508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in  Southeastern Brazil"],"prefix":"10.3390","volume":"7","author":[{"given":"Bruno","family":"Schultz","sequence":"first","affiliation":[{"name":"Divis\u00e3o de Sensoriamento Remoto (DSR), Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos, 12227-010 SP, Brazil"},{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6758-1207","authenticated-orcid":false,"given":"Markus","family":"Immitzer","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]},{"given":"Antonio","family":"Formaggio","sequence":"additional","affiliation":[{"name":"Divis\u00e3o de Sensoriamento Remoto (DSR), Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos, 12227-010 SP, Brazil"}]},{"given":"Ieda","family":"Sanches","sequence":"additional","affiliation":[{"name":"Divis\u00e3o de Sensoriamento Remoto (DSR), Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas 1758, S\u00e3o Jos\u00e9 dos Campos, 12227-010 SP, Brazil"}]},{"given":"Alfredo","family":"Luiz","sequence":"additional","affiliation":[{"name":"Embrapa Meio Ambiente, Caixa Postal 69, Jaguari\u00fana, 13820-000 SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences, Vienna (BOKU), Peter Jordan Strasse 82, 1190 Vienna, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2015,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.3390\/rs2061589","article-title":"Monitoring global croplands with coarse resolution earth observations: The Global Agriculture Monitoring (GLAM) project","volume":"2","author":"Justice","year":"2010","journal-title":"Remote Sens."},{"key":"ref_3","unstructured":"FAO Food and Agriculture Organization of the United Nations\u2014Statistics Division. 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