Simulation of multi-criteria mean Clustering method for satellite imagery of Bogd Khan mountain of Mongolia - Darkhijav, Bayanjargal; Jargalsaikhan, Davaajargal; Altangerel, Munkh-Erdene; Rentsen, Enkhbat (Proceedings of the Mongolian Academy of Sciences, 2025)
Traditionally, a variety of unsupervised methods have been used for analyzing remotely-sensed imagery. Although popular clustering techniques such as K-means, Mini-Batch K-means, and Fuzzy C-means are widely applied, their classification accuracy is often limited. This study proposes a new clustering method, called Multi-Criteria Mean Clustering (MCMC), to classify and extract forest areas from remote sensing images. The proposed method is based on a multi-criteria optimization framework, and leverages Pareto-optimal solutions arising from multiple clustering objectives. We show that K-means and Mini-Batch K-means can be viewed as specific instances of the proposed MCMC approach. For experimental evaluation, Root Mean Square Error (RMSE), Structural Similarity Index (SSIM), Overall Accuracy (OA), and Intersection over Union (IU) were used to assess performance. The results demonstrate that the proposed MCMC method provides more accurate and reliable classification, achieving lower RMSE, higher SSIM, higher OA, and higher IU compared to conventional clustering techniques. The study area is Bogd Khan Mountain, located in Central Mongolia, on the southern fringes of Ulaanbaatar. Sentinel-2B remote sensing data were employed for this research, and all computational experiments were conducted in Python Jupyter Notebook.