dc.contributor.author | Kazakevičiūtė-Januškevičienė, Girūta | |
dc.contributor.author | Janušonis, Edgaras | |
dc.contributor.author | Baušys, Romualdas | |
dc.contributor.author | Limba, Tadas | |
dc.contributor.author | Kiškis, Mindaugas | |
dc.date.accessioned | 2023-09-18T20:35:16Z | |
dc.date.available | 2023-09-18T20:35:16Z | |
dc.date.issued | 2020 | |
dc.identifier.other | (SCOPUS_ID)85098242921 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/151115 | |
dc.description.abstract | 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’s and Spearman’s 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 “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-24 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.source.uri | https://www.mdpi.com/2072-4292/12/24/4152 | |
dc.source.uri | https://doi.org/10.3390/rs12244152 | |
dc.title | Assessment of the segmentation of RGB remote sensing images: A subjective approach | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 60 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas Mykolo Romerio universitetas | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Mykolo Romerio universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.researchfield | N 009 - Informatika / Computer science | |
dc.subject.researchfield | S 003 - Vadyba / Management | |
dc.subject.vgtuprioritizedfields | IK0202 - Išmaniosios signalų apdorojimo ir ryšių technologijos / Smart Signal Processing and Telecommunication Technologies | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | satellite image segmentation | |
dc.subject.en | segmentation quality assessment | |
dc.subject.en | correlation analysis | |
dc.subject.en | objective quality metrics | |
dc.subject.en | subjective evaluation | |
dcterms.sourcetitle | Remote sensing: Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users | |
dc.description.issue | iss. 24 | |
dc.description.volume | vol. 12 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 2-s2.0-85098242921 | |
dc.identifier.doi | 85098242921 | |
dc.identifier.doi | 1 | |
dc.identifier.doi | 000603240100001 | |
dc.identifier.doi | 10.3390/rs12244152 | |
dc.identifier.elaba | 79437534 | |