Application Possibilities of Orthophoto Data Based on Spectral Fractal Structure Containing Boundary Conditions

The self-similar structure-based analysis of digital images offers many new practical possibilities. The fractal dimension is one of the most frequently measured parameters if we want to use image data in measurable analyses in metric spaces. In practice, the fractal dimension can be measured well in simple files containing only image data. In the case of complex image data structures defined in different metric spaces, their measurement in metric space encounters many difficulties. In this work, we provide a practical solution for the measurement of ortho-photos—as complex image data structures—based on the spectral fractal structure based on boundary conditions (height, time, and temperature), presenting the further development of the related theoretical foundations. We will discuss the optimal flight altitude determination in detail through practical examples. For this, in addition to the structural measurements on the images, we also use the well-known image entropy in information theory. The data obtained in this way can facilitate the optimal UAS operation execution that best suits further image processing tasks (e.g., classification, segmentation, and index analysis).