Multiresolution processing for fractal analysis of airborne remotely sensed data

Sandeep Jaggi; Dale A. Quattrochi; Nina Siu-Ngan Lam

doi:10.1117/12.138442

1 October 1992 Multiresolution processing for fractal analysis of airborne remotely sensed data

Sandeep Jaggi, Dale A. Quattrochi, Nina Siu-Ngan Lam

Proceedings Volume 1705, Visual Information Processing; (1992) https://doi.org/10.1117/12.138442
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Fractal geometry is increasingly becoming a useful tool for modeling natural phenomenon. As an alterative to Euclidean concepts, fractals allow for a more accurate representation of the nature of complexity in natural boundaries and surfaces. Since they are characterized by self- similarity, an ideal fractal surface is scale-independent; i.e. at different scales a fractal surface looks the same. This is not exactly true for natural surfaces. When viewed at different spatial resolutions parts of natural surfaces look alike in a statistical manner and only for a limited range of scales. In this paper, images acquired by NASA's Calibrated Airborne Multispectral Scanner are used to compute the fractal dimension as a function of spatial resolution. Three methods are used to determine the fractal dimension--Shelberg's line-divider method, the variogram method and the triangular prism method. A description of these methods and the result of applying these methods to a remotely-sensed image is also presented. The scanner data was acquired over western Puerto Rico in January, 1990 over land and water. The aim is to study impacts of man-induced changes on land that affect sedimentation into the near-shore environment. The data was obtained over the same area at 3 different pixel sizes--10 m, 20 m and 30 m.

Citation Download Citation

Sandeep Jaggi, Dale A. Quattrochi, and Nina Siu-Ngan Lam "Multiresolution processing for fractal analysis of airborne remotely sensed data", Proc. SPIE 1705, Visual Information Processing, (1 October 1992); https://doi.org/10.1117/12.138442

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