###### Abstract  
The combination of MCDM 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. Since no single segmentation algorithm can achieve the best results on satellite image datasets, 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 new 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 intuitive fuzzy sets (IFSs) 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. satellite imagery; image segmentation; segmentation quality assessment; multiple-criteria decision-making methods; PROMETHEE; intuitionistic fuzzy set Article  
Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method  
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to enhance segmentation accuracy, specifically if the object has a very small size and range area. Color-based segmentation can be effective and computationally efficient for certain applications. Especially for satellite images, color contains information correlating with human visual perception [5]. As humans naturally interpret and distinguish regions based on color, including color information in segmentation processes creates a more effective approach. The authors of [6] introduce a method for color image segmentation based on the visual attention mechanism. The low-level features of the image, including color information, are extracted to construct a saliency map that highlights the salient regions in the image, which aids in the subsequent segmentation process. Visual features of an image describe various aspects of the image content. Satellite images have distinct colors and textures as well as patterns and shapes. Volcanoes and craters are characterized by circular shapes, whereas mountain ranges typically extend in lengthy, occasionally wavy lines. This is an important factor in the selection of satellite image segmentation or processing methods. In addition, the MCDM methods [7] provide tools suitable for addressing numerous practical decision-making scenarios where the task involves identifying the optimal alternative amidst various competing criteria. An alternative, in the MCDM context, represents a candidate or a choice that is being considered for selection. From an image segmentation perspective, alternatives could be different segmentation algorithms that are being compared based on multiple criteria to determine their effectiveness in producing desired segmentation results. Criteria are the standards or measures used to evaluate and compare alternatives. In image segmentation, criteria can be image content features or objective quality metrics. The utilization of MCDM methods extends to diverse applications in various domains. MCDM methods, like a neutrosophic extension of the CoCoSo method [8], were used to select the best retrofit strategy. WASP-mGqNS [9] was utilized for ranking regions by minimizing the traveled distance and maximizing the exploration space of an autonomous robot. The mGqNN version of the PROMETIHEE multi-criteria method was used to calculate the trustworthiness index of the respondents participating in a game-based survey [10].