Multi-Resolution Pattern-based Segmentation of Very Large Raster Datasets

What:
Presentation
When:
Wednesday Sep 28   04:10 PM to 04:30 PM (20 minutes)
Tags:
segmentationlarge data setsCOBIA
Discussion:
0
We present an algorithm which efficiently segments very large categorical rasters based on patterns of their categories. It operates on a grid of motifels – square blocks of raster cells representing a local pattern. Our algorithm is based on the seeded region growing principle but it uses a novel grid topology and seeds stack with individual thresholds. It has a single free parameter – the spatial scale of a pattern. Algorithm was proven to be robust on land cover data, topographic landforms data, and high resolution color-quantized RGB images. We present a multi-scaled segmentation of NLCD2011 as an example. Potential applications of the new algorithm include ecology, geomorphology, pedology, forestry, agriculture, and urban studies.
Participant
Adam Mickiewicz University in Poznan
Participant
University of Cincinnati
Participant
University of Cincinnati

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