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Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation AlgorithmThe hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.
Document ID
Acquisition Source
Goddard Space Flight Center
Document Type
Book Chapter
Tilton, James C.
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Plaza, Antonio J.
(Extremadura Univ. Caceres, Spain)
Chang, Chein-I.
(Maryland Univ. Baltimore County Baltimore, MD, United States)
Date Acquired
August 25, 2013
Publication Date
January 1, 2008
Publication Information
Publication: International Journal of High Performance Computing Applications
Publisher: Taylor and Francis Group, LLC
Volume: 22
Issue: 4
ISBN: 78-1-58488-662
Subject Category
Earth Resources And Remote Sensing
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