Fractal representations in image processing of remote sensing of the earth
Abstract
This article explores the application of fractal representations in the image processing of remote sensing data for Earth observation. Fractals, with their self-similar properties and complex patterns, offer a powerful mathematical framework for analyzing the intricate structures found in natural landscapes. The study highlights the advantages of using fractal-based methods over traditional image processing techniques, particularly in capturing the multifaceted textures and irregularities of Earth’s surface features. By leveraging fractal geometry, enhanced accuracy in the classification and interpretation of remote sensing images is achieved. This approach facilitates better monitoring and understanding of environmental changes, land use patterns, and natural disasters. The findings underscore the potential of fractal representations to significantly improve the quality and efficacy of remote sensing image analysis, providing a robust tool for Earth science research and practical applications in environmental management.