Title Selection of segmentation algorithm for satellite images
Authors Janušonis, Edgaras ; Kazakevičiūtė-Januškevičienė, Girūta ; Baušys, Romualdas
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Is Part of ICFLIPA 2024: XVIII. International conference on fuzzy logic image processing applications, February 05-06, 2024 in Amsterdam, Netherlands.. [S. l.] : Word Academy of Science Engineering and Technology. 2024, p. 30.. eISSN 1307-6892
Keywords [eng] satellite imagery ; image segmentation ; segmentation quality assessment ; multiple-criteria decision-making methods ; PROMETHEE ; intuitionistic fuzzy set
Abstract [eng] The combination of multi-criteria decision-making (MCDM) methods and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. To our best knowledge no single segmentation algorithm can consistently achieve the best results on all satellite image datasets. Therefore, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitionistic fuzzy sets (IFS) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis. Average rank comparison and sensitivity analysis confirm that the proposed PROMETHEE-IFS method is well-suited for the selection of segmentation algorithms for satellite images.
Published [S. l.] : Word Academy of Science Engineering and Technology
Type Conference paper
Language English
Publication date 2024