Water Body Satellite Images; Image Thresholding; Minimum Cross Entropy; Maxwell-Boltzmann distribution; Iterative algorithm


Applied Mathematics | Computer Sciences


Images can exhibit diverse attributes and characteristics, because of variations in both the quantity of each intensity level and their respective positions, histograms display varying distributions. Some images feature symmetric histograms, while others exhibit asymmetry. In image segmentation tasks, traditional mean-based thresholding methods work well with symmetric histograms, relying on Gaussian distribution definitions. However, situations arise where asymmetric distributions must be considered. Threshold-based segmentation entails the partitioning of intensity levels into separate regions determined by the threshold value. Within this category of thresholding methods, Minimum Cross Entropy Thresholding (MCET) stands out as a mean-based thresholding technique with a unique self-contained objective function adaptable to various distributions. In our study, we propose incorporating the Maxwell-Boltzmann Distribution into MCET's objective function. We introduce a specialized model aimed at enhancing efficiency in image segmentation tasks, enabling precise data analysis customized to specific images with histograms skewed to the right. This approach yields improved segmentation results by considering the impact of utilizing the Maxwell-Boltzmann distribution with the right-skewed distribution within MCET's objective function. We have validated our approach, and we conducted a comparative analysis, assessing the performance of our proposed model against relevant studies in the literature. We applied this approach to Sentinel-2 satellite imagery for water body segmentation. The outcomes showcase the effectiveness of our model in segmenting images with right-skewed histograms, substantiated by a variety of performance evaluations.

Author ORCID Identifier

Ali ElZaart https://orcid.org/0000-0002-1240-3785

Lama Affara https://orcid.org/0000-0001-6950-5347

Rabih Damaj https://orcid.org/0000-0002-0371-9163





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