Abstract: The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary point values 14, 4, and 1. Particle Swarm Optimization was employed to systematically explore and optimize these point values, iteratively seeking an ideal configuration for the scoring system. After optimization, the Neighborhood Median Pixel Method exhibited a slight increase in overall accuracy. Using the 9x9 neighborhood size, the accuracy rose from 94% to 94.75%, while with the 13x13 neighborhood size, the accuracy improved from 95.75% to 96%. Furthermore, results indicate that varying point value configurations yield similar classification outcomes, suggesting that the method’s scoring system is robust across multiple configurations and that the originally proposed point value set remains adequate for effective classification.