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Multi-scale segmentation algorithm for pattern-based partitioning of large categorical rasters

Multi-scale segmentation algorithm for pattern-based partitioning of large categorical rasters

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Authors

Jaroslaw Jasiewicz, Tomasz Stepinski, Jacek Niesterowicz

Abstract

Analyzing large Earth Observation (EO) data on the broad spatial scales frequently involves regionalization of patterns. To automate this process we present a segmentation algorithm designed specifically to delineate segments containing quasi-stationary patterns. The algorithm is designed to work with patterns of a categorical variable. This makes it possible to analyze very large spatial datasets (for example, a global land cover) in their entirety. An input categorical raster is first tessellated into small square tiles to form a new, coarser, grid of tiles. A mosaic of categories within each tile forms a local pattern, and the segmentation...  more

DOI

https://doi.org/10.31223/osf.io/smva7

Subjects

Computer Sciences, Earth Sciences, Geography, Numerical Analysis and Scientific Computing, Other Earth Sciences, Physical Sciences and Mathematics, Social and Behavioral Sciences, Spatial Science

Keywords

Topography, land cover, Segmentation, categorical rasters, high resolution image, spatial patterns

Dates

Published: 2018-01-28 12:10

License

CC BY Attribution 4.0 International