## 2 Related Works
The authors of [33] introduce a group MCDM (GMCDM) framework for assessing and ranking clustering algorithms across diverse benchmark datasets, employing internal and external measures. As relying solely on a single ranking algorithm may lack robustness, three MCDM algorithms are used, and their ranks are merged using the Borda method to obtain the final ranking. For satellite images, the neutrosophic WASP [34] method was used to select the best edge detection algorithm based on visual features of satellite images, such as contrast, roughness, and edge density evaluated by four user groups. The reliability of the methodology was validated through expert assessments. Similarly, the authors of [35] introduced a novel methodology for the qualitative selection of lossy compression techniques for aerial images. The suitability of transform-based lossy compression algorithms with specific compression ratios was assessed according to image resolution and content. These algorithms were ranked for aerial images using WASP-SVNS with a direct criteria weight evaluation method. The quality of the segmentation results can be significantly influenced by the selection of the optimal number of clusters. MCDM methods such as the WSM (Weighted Sum Model), PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation), and TOPSIS have been used to estimate the optimal number of clusters for a given dataset [36]. The authors proposed an MCDM-based approach for estimating the optimal number of clusters in a data set, which treats different numbers of clusters as alternatives and clustering validity measures as criteria. The comparative study demonstrates that the three MCDM methods (WSM, TOPSIS, and PROMETHEE II) surpass existing relative measures. Segmentation quality is influenced by various other factors, such as preprocessing, segmentation parameters, and color space. In a recent paper [37], the authors developed a genetic algorithm designed to select optimal segmentation methods and their optimal segmentation parameters and color space. The authors of [38] proposed a selection algorithm that uses a deep neural network (DNN) to select the optimal segmentation results by predicting the optimal number of segmentation iterations. The existing methods mentioned earlier do not fully address several common issues, such as the need to train large models that demand substantial computational resources, working with a small dataset, analyzing an unconventional image content type(s), or lacking ground truth data that is often unavailable for satellite image datasets or is inaccurate. Various methods were employed to select the optimal segmentation algorithm, including comparative and content analysis and machine learning-based methodologies. A particular aspect the existing methods might not have fully evaluated is the need for a unified approach combining several criteria. While some methods consider expert opinions, extract statistical features, or use several objective metrics simultaneously, there is a gap in evaluating image segmentation algorithms that could merge expert knowledge with visual image features, providing more specialized decision-making for satellite images.