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Fractal models for analysis of remote sensing data

Research has been undertaken to explore how fractal methods can be used to assist in the understanding of satellite imagery. An extensive bibliographic search was carried out on the existing uses of fractals in remote sensing and the geosciences. The following main applications were identified:
characterization of terrain angularity or roughness, including mountains, ice and snow surfaces, geomorphological processes, application to digital terrain model quality evaluation and cartographic generalization;
characterization of 2-dimensional image textures, including the linking of fractal textures to human visual perception of texture, use of fractal textures to improve land use classification in satellite imagery and use of fractal textures for image segmentation;
characterization of climatic and atmospheric phenomena;
image compression using fractal models based on iterated function systems (IFS), including use of neural networks for IFS generation;
synthetic image generation. Soil erosion prediction was investigated by fractal surface analysis to see if the fractal analysis of pixel brightness in airborne and satellite imagery could be used directly to infer the topographic properties of bare or partially denuded soil surfaces without the need for complex stereo image acquisition and analysis, especially in zones prone to soil erosion. A project has been set up aimed at the development of software tools for measuring fractal dimension in remotely sensed images.


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