## 6 Conclusions and Future Work  
Image segmentation algorithms may not always return accurate results due to the image complexity and different image content. Therefore, identifying the most appropriate method for the specific image content is often essential. This paper proposed an approach that extends the PROMETHEE method via intuitionistic fuzzy sets and combines it with the SWARA weighting model for selecting optimal segmentation methods for satellite images. This study used five subjective visual features: contrast, roughness, regions of very small size, distortions of regions, and smoothness of region edges. A novel way of forming a decision matrix was used by combining information from two different sources (image assessment vector and the fuzzy algorithm evaluation matrix). 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.  
Image content characteristics are often interrelated. Changing one image feature leads to changes in other image features (increased contrast can create more separation between adjacent colors). Different segmentation algorithms have varying sensitivity and interpretation of image content features. Thus, no one algorithm fits all content. Both the results of this experiment and the verification confirm the NFL theorem [58] in the context of image segmentation. This again underlines the importance of considering the characteristics of the images when choosing or developing segmentation algorithms.  
This methodology can be used with other subjective visual features; however, the features must have a clearly defined optimization direction, i.e., minimum and maximum values. If a feature does not have a logical minimum or maximum value that could affect the quality of segmentation, such a criterion cannot be easily included in the initial decision matrix. For example, mathematically quantifying the shape of clusters (as a visual feature) in a way that applies universally across diverse image content and domains might be challenging.  
A single image may not fully represent specific features or the class it belongs to. This is especially true for general-purpose image datasets that lack specific categorization or are not created for a particular task or application. Applying the suggested method to a larger set of images might be necessary in such a case.  
The following further research perspectives may be included:  
* Assessing the impact of optimal segmentation method selection under different score functions [59] or other normalization methods of the decision-making matrix;
* Although we have chosen criteria that reflect the main aspects of segmentation, it is worth exploring other specific criteria/metrics when developing the methodology.  
In general, MCDM methods can be applied to optimize the segmentation algorithm, particularly fuzzy logic-based segmentation methods [60]. By incorporating MCDM methods, these algorithms can be further optimized to provide more accurate segmentation results. Finally, the proposed approach can be adapted to select the optimal number of clusters problem, where within a given dataset, different numbers of clusters would be represented as potential alternatives.  
Writing--review and editing, investigation, formal analysis, validation, methodology, conceptualization, G.K.-J., E.J. and R.B.; software, E.J. and R.B.; resources, E.J.; data curation, G.K.-J. and E.J.; writing--original draft preparation, E.J.; supervision, G.K.-J. and R.B.; project administration, R.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.