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Title: Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach
Authors: Kazakevičiūtė-Januškevičienė, Girūta
Janušonis, Edgaras
Baušys, Romualdas
Limba, Tadas
Kiškis, Mindaugas
Keywords: satellite image segmentation
segmentation quality assessment
correlation analysis
objective quality metrics
subjective evaluation
Issue Date: 2020
Publisher: MDPI
Citation: Kazakeviciute-Januskeviciene, G.; Janusonis, E.; Bausys, R.; Limba, T.; Kiskis, M. Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach. Remote Sens. 2020, 12, 4152.
Series/Report no.: 12;24
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.
Description: This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to Users
ISSN: 2072-4292
Appears in Collections:Moksliniai straipsniai / Research articles

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