{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:03:29Z","timestamp":1780401809612,"version":"3.54.1"},"reference-count":60,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,18]],"date-time":"2020-12-18T00:00:00Z","timestamp":1608249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Council of Lithuania (LMTLT)","award":["S-MIP-19-27"],"award-info":[{"award-number":["S-MIP-19-27"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson\u2019s and Spearman\u2019s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the \u201cDeepGlobe Land Cover Classification Challenge\u201d dataset was constructed for testing three classes of quality metrics for satellite image segmentation.<\/jats:p>","DOI":"10.3390\/rs12244152","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T01:01:08Z","timestamp":1608512468000},"page":"4152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach"],"prefix":"10.3390","volume":"12","author":[{"given":"Giruta","family":"Kazakeviciute-Januskeviciene","sequence":"first","affiliation":[{"name":"Department of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania"},{"name":"Faculty of Public Governance and Business, Mykolas Romeris University, Ateities str. 20, LT-08303 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edgaras","family":"Janusonis","sequence":"additional","affiliation":[{"name":"Department of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6284-0406","authenticated-orcid":false,"given":"Romualdas","family":"Bausys","sequence":"additional","affiliation":[{"name":"Department of Graphical Systems, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tadas","family":"Limba","sequence":"additional","affiliation":[{"name":"Faculty of Public Governance and Business, Mykolas Romeris University, Ateities str. 20, LT-08303 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mindaugas","family":"Kiskis","sequence":"additional","affiliation":[{"name":"Faculty of Public Governance and Business, Mykolas Romeris University, Ateities str. 20, LT-08303 Vilnius, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. 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