The objective of this study is to utilize lacunarity as a statistical measure for assessing image heterogeneity in four Regions of Interest (ROIs) extracted from Indian Pines, with a specific focus on band 29 chosen as the “maximum information band.” The study employs an algorithm that systematically processes sub-images within defined ROIs to evaluate textural characteristics at multiple spatial scales. The band selection is based on statistical analysis, specifically identifying the band with the highest standard deviation. ROI 1 is observed to consistently exhibit high lacunarity, indicating a complex and varied texture with a variety of land cover types. ROI 2 exhibits significant lacunarity with a downward trend, suggesting a more organized texture influenced by specific constituents. ROI 3 exhibits a declining trend with relatively high lacunarity, suggesting a complex, multidimensional structure. ROI 4 has moderate to high lacunarity, indicating that its slightly uneven and textured appearance is the result of a mixture of components. In addition to offering a thorough technique and formulas for calculating lacunarity, the research gives insightful information about the spatial properties of hyperspectral data. The research establishes the foundation for gliding box lacunarity’s potential uses in remote sensing and image processing, specifically in the areas of agriculture, ecology, and land management decision-making.