Je. Peak et Pm. Tag, SEGMENTATION OF SATELLITE IMAGERY USING HIERARCHICAL THRESHOLDING ANDNEURAL NETWORKS, Journal of applied meteorology, 33(5), 1994, pp. 605-616
A significant task in the automated interpretation of cloud features o
n satellite imagery is the segmentation of the image into separate clo
ud features to be identified. A new technique, hierarchical threshold
segmentation (HTS), is presented. In HTS, region boundaries are define
d over a range of gray-shade thresholds. The hierarchy of the spatial
relationships between collocated regions from different thresholds is
represented in tree form. This tree is pruned, using a neural network,
such that the regions of appropriate sizes and shapes are isolated. T
hese various regions from the pruned tree are then collected to form t
he final segmentation of the entire image. In segmentation testing usi
ng Geostationary Operational Environmental Satellite data, HTS selecte
d 94% of 101 dependent sample pruning points correctly, and 93% of 105
independent sample pruning points. Using Advanced Very High Resolutio
n Radiometer data, HTS correctly selected 90% of both the 235-case dep
endent sample and the 253-case independent sample pruning points. The
strength of this approach is that artificial intelligence, that is, re
asoning about the sizes and shapes of the emergent regions, is applied
during the segmentation process. The neural network component can be
trained to respond more favorably to shapes of interest to a particula
r analysis problem.