Target extraction using hierarchical clustering with refinement by probabilistic relaxation labeling

Timothy S. Newman; Jinsoo Lee; Scott R. Vechinski

doi:10.1117/12.323860

18 September 1998 Target extraction using hierarchical clustering with refinement by probabilistic relaxation labeling

Timothy S. Newman, Jinsoo Lee, Scott R. Vechinski

Proceedings Volume 3371, Automatic Target Recognition VIII; (1998) https://doi.org/10.1117/12.323860
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Timothy S. Newman, Jinsoo Lee, and Scott R. Vechinski "Target extraction using hierarchical clustering with refinement by probabilistic relaxation labeling", Proc. SPIE 3371, Automatic Target Recognition VIII, (18 September 1998); https://doi.org/10.1117/12.323860

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