Multiscale and Multigranular (MSMG) Spatial Data Mining (SDM) algorithms are used to find the best granular class label from a hierarchical set of granular class labels for spatial classification, which is important for many application domains including the military. However, it is computationally very expensive due to a complex quality measure for ranking class labels. In this paper we propose a parallel formulation of a MSMGSDM algorithm to scale up to the problem sizes of interest to the Army using the Partitioned Global Address Space (PGAS) model programmed in Unified Parallel C (UPC), which facilitates sharing of data among processors. Experimental evaluations for land cover classification from satellite imagery show that the proposed parallel formulation achieves speedup of 6.65 using 8 processors.
[1]
Josiane Zerubia,et al.
A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification
,
1996,
CVGIP Graph. Model. Image Process..
[2]
R. A. Vaughan,et al.
The effect of scaling on land cover classification from satellite data
,
2003
.
[3]
Shashi Shekhar,et al.
Spatial Databases: A Tour
,
2003
.
[4]
J. Townshend,et al.
Land cover classification accuracy as a function of sensor spatial resolution
,
1981
.
[5]
A. Willsky.
Multiresolution Markov models for signal and image processing
,
2002,
Proc. IEEE.
[6]
Eric D. Kolaczyk,et al.
Multiscale, Multigranular Statistical Image Segmentation
,
2005
.