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Proceedings Paper

Target extraction using hierarchical clustering with refinement by probabilistic relaxation labeling
Author(s): Timothy S. Newman; Jinsoo Lee; Scott R. Vechinski
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Paper Abstract

A new multi-stage technique is presented for segmentation of targets of interest in synthetic aperture radar (SAR) data. The method creates an initial coarse segmentation using a histogram-based approach that labels each pixel as foreground or background. The extents of targets of interest are then determined using a hierarchical clustering stage that utilizes a novel weighting of intensity and pixel position. Finally, each potential target's segmentation is improved using probabilistic relaxation labeling. The approach loosens the typical region-based segmentation paradigm that only contiguous pixels can compose a segment. The technique is useful both for target segmentation and as a pre-processing step to verify the fidelity of artificially-generated data with real data.

Paper Details

Date Published: 18 September 1998
PDF: 8 pages
Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); doi: 10.1117/12.323860
Show Author Affiliations
Timothy S. Newman, Univ. of Alabama in Huntsville (United States)
Jinsoo Lee, Univ. of Alabama in Huntsville (United States)
Scott R. Vechinski, Science Applications International Corp. (United States)


Published in SPIE Proceedings Vol. 3371:
Automatic Target Recognition VIII
Firooz A. Sadjadi, Editor(s)

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