ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 75–81, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-75-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 75–81, 2022
https://doi.org/10.5194/isprs-annals-V-4-2022-75-2022
 
18 May 2022
18 May 2022

EVALUATION METHOD FOR INFORMATION CONTENT OF RASTER DATA USING FRACTAL DIMENSION

T. Osaragi T. Osaragi
  • School of Environment and Society, Tokyo Institute of Technology, Japan

Keywords: raster data, geographic feature, information theory, fractal dimension, cell size, noisy channel

Abstract. Raster data are obtained by dividing an entire study area into a regular grid of cells. Hence, raster data information depends on cell size as well as the shapes of geographical features to be rasterized. In this paper, we report on the construction of a method for evaluating the information content obtained from raster data using information theory. We also provide some numerical examples showing that the resulting information content can be expressed as a logistic function of cell size and that its parameters can be written as simple linear models with the fractal dimensions of the geographic features to be rasterized.